A lot has been happening in my life lately, in particular in my creative life. In the last week I moved to Asheville, NC. In addition to this Substack, I also publish Zen Mountain Journal. It has taken off lately, and between the move and packing and shipping art orders, I haven’t worked much on Emerging Cures.

Emerging Cures is/was an experiment that didn’t work. The reaction to it has been lukewarm. It would take a lot of work to make it viable. I’d either have to drop Zen Mountain Journal or work 100 hours a week. I may, at some future time, come back to it, but that is unlikely in the short term.

Having said all of that, I believe that medical science is on the verge of solving cancer and most other major diseases and while getting access to the new therapies is difficult it is not impossible. If I was a patient, that would certainly be where I’d focus my efforts.

AI is helping medical researchers understand a wide range of deadly diseases on a molecular level, and develop new effective treatments in hours or days instead of years. In the last two weeks, for instance, Google’s Deepmind announced that it has developed an AI program to virtually test individual tumors against 4000 different drug candidates to determine the optimum combination to fight that tumor’s specific genetic characteristics. Regardless of whether or not this test proves as effective as it now appears to be, this approach, perhaps in a modified form, will ultimately change everything in cancer treatment and outcomes.

The screenshot above is from Peter Diamandis’s YouTube podcast Moonshots, my favorite on the subject of the evolving impact of AI on science and culture. I believe it is from the October 23 post.

Getting access to these new therapies requires resourcefulness. Perhaps resourcefulness is something a person either has or doesn’t have. I hoped to provide it as a service in Emerging Cures. I now question whether or not that is a viable concept.

At the end of this month, November 2025, I’ll shut down the Emerging Cures website, phone numbers and email addresses. In the meantime, you can reach me at

research@emergingcures.org

And thank you for your interest in this work.

. . .

Our new book, “Surviving Cancer: Hope based on emerging medical science” is now live on Amazon and available to order here.

You can read about my own cancer journey (My History With Terminal Cancer) here.

With this week’s article, we conclude our series on new treatments that appear to be on the cusp of solving life-threatening cancers. Both of the therapies profiled in this article show potential on their own, but appear most effective when used in combination with other proven therapies, often dramatically boosting response rates.

How ultrasound is ushering in a new era of surgery-free cancer treatment

Ultrasound has long been used to help doctors see inside the body, but high-frequency sound waves are now being turned into weapons against cancer itself. A new generation of scientists is using sound to destroy tumors, open the blood–brain barrier, and even awaken the immune system—all without a single incision.

It began, as breakthroughs often do, with an accident. In the early 2000s, Zhen Xu, then a PhD student at the University of Michigan, was experimenting with powerful ultrasound pulses on pig hearts. Her aim was to find a non-surgical way to remove diseased tissue. The amplifier she used was so loud that labmates complained. To spare them, she shortened her ultrasound pulses to microseconds—rendering them inaudible. To her astonishment, the shorter bursts worked far better, mechanically breaking apart heart tissue in seconds.

That moment led to histotripsy, a method that destroys tumors through rapid cavitation—creating and collapsing microscopic bubbles that shred diseased tissue while sparing surrounding structures. The immune system then clears away the remnants naturally.

From experiment to clinic

Two decades later, Xu’s discovery has become an approved medical technology. In 2023, the U.S. Food and Drug Administration approved histotripsy for liver tumors, and clinical trials have shown 95 % success in completely ablating targeted lesions. The U.K. National Health Service began offering it in 2024 under its Innovative Devices Access Pathway for unmet clinical needs.

“People think of ultrasound as imaging,” says Julie Earl, a principal investigator at Spain’s Ramón y Cajal Institute. “But research shows it can also destroy tumors, slow metastases, and enhance the effects of other treatments—all without a knife.”

During treatment, robotic arms guide ultrasound transducers to focus energy on a pinpoint—roughly the size of a pen tip. Each microsecond pulse forms and collapses microbubbles that tear apart tumor cells. Most patients go home the same day. A session typically lasts one to three hours, and many need only a single treatment.

There are still questions. Bone and air can block sound waves, limiting the technique’s use in the lungs or near the skull, and long-term data on cancer recurrence are still being gathered. Yet ongoing trials are exploring kidney and pancreatic tumors, and early results show a low rate of complications.

Cooking cancers with sound

Histotripsy builds on an older technology known as High-Intensity Focused Ultrasound (HIFU), which attacks tumors with heat rather than mechanical energy. “It’s like holding a magnifying glass over a leaf,” says Richard Price of the University of Virginia’s Focused Ultrasound Cancer Immunotherapy Center. “The sound energy converges to a focal point and literally cooks the tissue.”

HIFU is already widely used for prostate cancer, where it offers similar effectiveness to surgery but with quicker recovery and fewer side effects. Like histotripsy, it’s performed under general anesthesia to keep patients still, but unlike histotripsy, HIFU generates heat that can occasionally damage nearby healthy tissue. Researchers are now adapting HIFU for breast and soft-tissue cancers, and robotic systems at Cleveland Clinic London and Virginia Commonwealth’s Massey Cancer Center are pioneering precision versions for prostate treatment.

Sound-enhanced therapies

The power of ultrasound may extend far beyond ablation. Scientists are learning that the same waves can open biological barriers and amplify other cancer treatments.

In Canada, Deepa Sharma at Sunnybrook Health Sciences Centre has shown that injecting microbubbles into the bloodstream and activating them with ultrasound can temporarily open the blood–brain barrier. This allows chemotherapy or targeted drugs to reach brain tumors that would otherwise be shielded. The same approach can improve drug delivery to other tumor types while reducing systemic toxicity.

Her group also found that ultrasound-stimulated microbubbles can enhance radiation therapy by damaging tumor blood vessels, increasing cell death and allowing doctors to use lower doses of radiation. “Radiation cures cancer,” Sharma says, “but it causes many long-term side effects. If ultrasound lets us reduce the dose, patients benefit twice.”

Awakening the immune system

Researchers are also exploring ultrasound as an immune catalyst. When tumors are disrupted by sound waves, their debris exposes hidden antigens to the immune system, which can then recognize and attack similar cancer cells elsewhere in the body. Price’s group at the University of Virginia is testing whether combining ultrasound with immunotherapies like anti-PD-1 drugs can spark a systemic response.

“The dream,” he says, “is that treating one tumor teaches the immune system to find the rest.”

Clinical trials launched in late 2024 are testing this hypothesis in melanoma and metastatic prostate cancer, pairing focused ultrasound with checkpoint inhibitors to see whether immune activation extends survival. Early preclinical results are encouraging.

Emerging frontiers (2024–2025)

Recent studies show ultrasound’s potential is expanding even faster than anticipated:

• Immune modulation: At Northeastern University, scientists are using low-intensity ultrasound to recruit macrophages and T cells into tumors, potentially converting “cold” immune-resistant cancers into “hot” ones that respond to therapy.

• Ultrasound-activated drug delivery: Teams worldwide are developing nanoparticles and liposomes that release chemotherapy drugs only when exposed to ultrasound, improving precision and safety. One 2025 study demonstrated a “smart” immune-cell-targeted nanoparticle that releases its payload on command.

• Oncotripsy and low-intensity approaches: New modeling work suggests that cancer cells resonate differently from healthy ones. Ultrasound tuned to those frequencies can selectively destroy malignant cells—a concept dubbed oncotripsy—without heating or cutting tissue.

• Sonodynamic therapy (SDT): Combining ultrasound with sonosensitizing molecules that generate reactive oxygen species inside tumors is showing promise for deep, hard-to-reach cancers like pancreatic and glioblastoma.

• Autonomous and robotic systems: Prototype robots can now scan, locate, and target lesions using ultrasound imaging with minimal human input—pointing toward an era of AI-guided, incision-free oncology.

• New clinical indications: In 2025, the Focused Ultrasound Foundation announced nine new preclinical programs for pediatric brain tumors, melanoma, and breast cancer. Hospitals such as St. David’s Medical Center in Texas have begun using FDA-approved histotripsy devices for liver cancer, while European centers expand use to pancreas and kidney.

Promise and caution

The pace of innovation is remarkable, yet researchers urge caution. Ultrasound therapies remain complex, requiring precise calibration to avoid damage to surrounding tissues. And while results are promising, they won’t replace surgery or chemotherapy for all patients—at least not yet.

Still, the shift is unmistakable. Once merely a diagnostic tool, ultrasound is becoming a therapeutic platform: a way to break tumors apart, open the brain to medicine, and enlist the immune system—all guided by sound.

“Cancer is awful,” Xu says. “What makes it even worse is the suffering caused by treatment. If we can use sound to heal instead of harm, that’s a future worth building.”

mRNA cancer vaccines may provide a universal immune cure

I’m constantly reminding myself – and occasionally find myself telling others – that cancer isn’t a single disease but rather a vast array of diseases with some similar characteristics. Recent research into mRNA vaccines is calling into question that assertion.

Cancers are difficult to treat because tumors have built-in characteristics that confuse our immune systems. Tumors cloak themselves in a biochemical fog that turns immune cells from protectors into bystanders. Now, researchers are discovering that messenger RNA—the same genetic courier used in COVID-19 vaccines—can pierce that fog and reawaken the immune system’s natural vigilance. Across multiple continents, new mRNA cancer vaccines are beginning to show what many once thought impossible: training the body itself to recognize and destroy cancer.

Awakening the immune system

At the University of Florida, scientists recently developed an experimental mRNA vaccine that destroyed skin, bone, and brain tumors in mice when paired with existing immunotherapy drugs. The approach doesn’t attack the cancer directly. Instead, it teaches the immune system to see tumor cells.

Cancer’s defensive barrier, known as the tumor microenvironment, is packed with molecules that suppress T-cells and block cytokine signals that would otherwise alert the body to danger.

Traditional immune checkpoint inhibitors—drugs like pembrolizumab (Keytruda)—work only when T-cells already recognize the tumor. The Florida team’s vaccine aimed to “wake up” the immune system first, so immunotherapy could finish the job.

A viral-style alarm

The vaccine uses mRNA encapsulated in lipid nanoparticles, similar to COVID-19 vaccines. Rather than coding for a viral spike protein, it encodes immune-signaling proteins that prompt a “virus-like” alarm throughout the body. This triggers production of interferons, messenger proteins that summon immune cells to infected—or cancerous—tissue.

When the vaccine was combined with PD-1 inhibitors, tumors in mice were eliminated. Even given alone, it sometimes wiped out resistant cancers. Dr. Elias Sayour, who led the study, called the results “unexpected and exciting.” The work, published in Nature Biomedical Engineering, showed that a non-specific mRNA vaccine could sensitize the immune system to diverse tumors, effectively transforming cold, immune-resistant cancers into hot, attackable targets.

Sayour described it as “proof of concept” for a universal cancer vaccine—a single mRNA platform that could be tuned to stimulate immunity against many types of tumors without needing to identify a specific mutation first.

Beyond personalization

Cancer vaccine science has traditionally followed two paths: either designing vaccines against shared targets found in many cancers, or crafting custom vaccines based on each patient’s unique tumor mutations. The Florida study revealed a third approach—a generalized mRNA vaccine that doesn’t target cancer directly but stimulates such a strong immune response that dormant T-cells suddenly recognize and destroy it.

“This could be an off-the-shelf strategy,” said Duane Mitchell, co-director of UF’s Preston A. Wells Jr. Center for Brain Tumor Therapy. “By generating a broad immunologic storm, you can awaken the body’s defenses instead of programming them cell by cell.”

Sayour’s lab had already demonstrated this concept in humans with a personalized mRNA vaccine for glioblastoma, which reprogramed the immune system within days and showed signs of prolonging survival. The new “generalized” vaccine goes further—replacing custom tumor sequencing with a single, broadly applicable formulation.

From concept to clinic

While the UF work remains in animals, human trials of mRNA cancer vaccines are now multiplying worldwide. The most advanced involve personalized mRNA vaccines combined with immunotherapy—and early results are promising.

In a Phase II melanoma trial, patients who received a personalized mRNA vaccine plus pembrolizumab had a 44 % reduction in recurrence compared to immunotherapy alone. Some participants have remained cancer-free for more than three years, suggesting the response is durable.

Another study in pancreatic cancer patients showed that an individualized vaccine (autogene cevumeran) produced strong T-cell responses and signs of long-term immune memory—a rare achievement in one of medicine’s most lethal diseases.

These results mark a turning point: mRNA cancer vaccines are no longer speculative—they are demonstrating measurable immunologic effects in humans.

Expanding the reach

Internationally, new programs are testing whether the same principles can work beyond melanoma and brain tumors.

• Lung cancer: BioNTech, the German firm behind the Pfizer COVID-19 vaccine, launched the world’s first mRNA lung cancer vaccine trial in 2024, enrolling more than 100 patients across seven countries. The vaccine, BNT116, is designed to treat or prevent recurrence of non-small-cell lung cancer, often in combination with checkpoint inhibitors.

• China: CSPC Pharma received approval to begin human clinical trials of an mRNA-based therapy (SYS6020) targeting multiple myeloma, marking one of the first China-developed RNA oncology programs to reach clinical testing.

• Breast and ovarian cancers: Several research groups are developing mRNA vaccines targeting HER2-positive and triple-negative tumors—aggressive subtypes that often relapse after chemotherapy.

• Australia: Trials on the Gold Coast are exploring mRNA vaccines to treat or prevent the recurrence of glioblastoma and other brain cancers, building on prior UF and BioNTech advances.

Together, these efforts signal the birth of a global mRNA oncology ecosystem, linking hospitals, biotech startups, and major pharmaceutical firms across the U.S., Europe, and Asia.

Innovations behind the scenes

Advances in computational biology are also fueling progress. New mRNA design algorithms can optimize codon usage, improve folding stability, and control the strength of immune activation, making vaccines both safer and more potent. Researchers are using machine learning to identify early immune-signature biomarkers that predict whether a vaccine will succeed, reducing trial times and improving patient selection.

At the same time, scientists are experimenting with mRNA drug delivery to specific organs—for instance, nanoparticles that focus on lymph nodes or bone marrow—turning the body’s immune hubs into manufacturing sites for anti-cancer proteins.

Promise and obstacles

Despite the excitement, challenges remain. Funding fluctuations in the United States, including a 2025 decision to wind down some federal mRNA programs, have raised concerns about sustaining momentum outside infectious-disease research. Technically, the hurdles are substantial: mRNA molecules degrade quickly, require precise lipid nanoparticle delivery, and can trigger unwanted inflammation if overactivated.

Yet most experts believe the field will continue to expand. The speed and adaptability of mRNA—scientists can design a new vaccine in weeks—make it ideal for rapidly evolving cancers. And as manufacturing costs drop, individualized vaccines could one day be produced locally within hospital labs.

Toward a universal vaccine

What excites researchers most is the growing evidence that mRNA vaccines can serve as immune amplifiers—not just for one cancer type, but for many. By priming the immune system with a “viral-style” alert, they appear to help checkpoint inhibitors, radiation, and even traditional chemotherapy work more effectively.

If this effect proves consistent in people, an off-the-shelf mRNA vaccine could become a companion therapy for nearly any solid tumor—a safe, repeatable way to rouse the immune system whenever cancer tries to return.

Dr. Mitchell put it simply: “It could be a universal way of waking up the body’s own response to cancer. And that would be profound.”

A new era for medicine

Twenty years ago, cancer vaccines were considered a dead end. Today, they are among the most dynamic areas in oncology research. From Florida’s universal mRNA mouse vaccine to BioNTech’s lung cancer trials, to early signs of success in pancreatic and brain cancers, mRNA increasingly seems to offer the long-sought solution to cancer’s evasive ability—it appears to offer a way of teaching the immune system to heal itself. These vaccines now could mark one of the great medical turning points of our time: a shift from killing cancer with toxic chemicals to coaxing the body to remember, respond, and resist.

If you or a loved one is a cancer patient and are interested in accessing one or more of the therapies described above, experimental or now approved for use but not widely available, and are unsure how to do that, feel free to contact Emerging Cures. Persistent digging to find obscure clinical trials and other elusive facts is one of our specialties.

This article wraps up, at least for the next couple of months, our reports on emerging cancer drugs and therapies. Next week, we’ll move on to ALS/Lou Gehrig’s disease and from there the emerging medical science for the treatment of Alzheimer’s, MS and Parkinson’s.

Be in touch if you’ve received a serious diagnosis and would like to talk.

‪(843) 284-6850‬ (Office)
(843) 802-0183 (Mobile)

research@emergingcures.org

. . .

Our new book, “Surviving Cancer: Hope based on emerging medical science” is now live on Amazon and available to order here.

If you or a loved one is dealing with a diagnosis that you would like to learn more about, you can order the Leading Edge report on the latest medical science, customized to that specific diagnosis, here.

Email me at Emerging Cures if you’d like to talk, rod@emergingcures.org.

You can read about my own cancer journey (My History With Terminal Cancer) here.

At Massachusetts General Hospital, a radiologist once recalled a mammogram that looked perfectly normal to the human eye. The image was nearly unremarkable, the sort of case that might have been signed off without hesitation. But the AI system reviewing it raised a flag: high risk. A second read confirmed the machine’s suspicion. Hidden in the corner of the breast was a tiny lesion—an early cancer, invisible in plain sight, that would have been missed.

For pathologists and radiologists alike, the borderline cases, when noticed, are the troubling ones. The tiny clusters of atypical cells that might mean nothing—or might mean everything. The sliver of tissue where a single misplaced judgment could determine a patient’s fate. False positives risk unnecessary fear and procedures. False negatives risk lives lost. The stakes are immense.

The rise of AI in pathology

In 2016, Dr. Lily Peng, then a computer scientist at Google, was already known for her groundbreaking work using AI to detect diabetic retinopathy from retinal images. But she soon turned to one of medicine’s most complex challenges: cancer pathology.

“Cancer is not one disease,” Peng explained. “It’s hundreds of diseases, each with countless variations. The complexity is staggering—but that’s exactly where AI can make the biggest impact.”

Her team partnered with Memorial Sloan Kettering Cancer Center, home to some of the world’s most experienced pathologists. Among them was Dr. David Klimstra, who had spent decades peering through microscopes at tissue slides. Initially skeptical, he wondered: How could a computer possibly grasp what I’ve spent 30 years training my eyes to see?

But as the models improved, Klimstra began to change his mind. The AI didn’t just match human accuracy—it sometimes found new prognostic markers: subtle patterns in cells that predicted tumor aggressiveness. Features even seasoned pathologists had never noticed.

“It wasn’t replacing human expertise,” Klimstra reflected. “It was augmenting it—helping us see things we didn’t know we were missing.”

From microscope to monitor

The shift began when glass slides were digitized. Once a tissue sample could be scanned into a gigapixel image, algorithms could comb through it as systematically as a radiologist scrolling through a CT scan.

One of the pioneers in this space was Thomas Fuchs, a computational pathologist who helped develop Paige Prostate, an AI tool that became the first to receive FDA authorization for assisting in cancer detection. In clinical trials, Paige was able to highlight suspicious regions on prostate slides with accuracy rivaling expert pathologists.

Recent breakthrough: foundation models transform pathology

In 2024, the field witnessed a paradigm shift with the emergence of foundation models specifically designed for pathology. Dr. Faisal Mahmood’s team at Harvard released UNI, a general-purpose model trained on over 100 million images from 100,000 slides representing both diseased and healthy organs. This was followed by PathChat, an AI assistant that could have “conversations” about uploaded pathology images and generate reports—receiving FDA breakthrough device designation within months of its release.

The impact was immediate. At Cleveland Clinic, Dr. Daniel Roberts observed that these AI copilots were streamlining laboratory workflows in ways previously unimaginable. “Tools for biomarker interpretation remain at the forefront,” Roberts noted, “but we’re seeing a growing number of solutions for workflow efficiencies at every step, from slide preparation to case signout.”

For everyday practice, this meant:

• Earlier detection of prostate cancers that might otherwise slip past.

• Faster identification of tiny breast cancer metastases in lymph nodes.

• Minutes saved per case on routine scanning—time that could instead be spent on the rare and difficult cases that truly required expert judgment.

• Foundation models that could analyze any tissue type without specific training, adapting to rare cancers previously underrepresented in datasets.

What once felt like an endless stream of microscope slides became a workflow where AI did the first pass, flagging what needed human attention. Pathologists were no longer needle-hunting in haystacks.

Clinical impact

AI-powered pathology has shown particular strength in the places where human judgment is most fragile: the borderline calls.

• In breast cancer screening, deep learning systems have outperformed radiologists in both UK and US datasets, reducing both false positives and false negatives.

• In prostate cancer, AI overlays can help distinguish ambiguous, borderline cases of glandular irregularity that previously led to diagnostic disagreements.

• In cervical pathology, early trials suggest AI may outperform generalist pathologists in spotting precancerous lesions.

The CAMELYON challenge: a turning point

The CAMELYON16/17 challenge marked a watershed moment in digital pathology. AI models tasked with detecting breast cancer metastases in lymph nodes not only matched but exceeded the performance of a panel of pathologists working under time constraints designed to mimic routine workflows. Google’s LYNA (LYmph Node Assistant) emerged from this challenge, enabling pathologists to detect significantly more micrometastases with shorter review times.

At Stanford, pediatric radiologist Dr. Kristen Yeom faced a particularly thorny problem: brain tumors in children. Because they are rare, most radiologists see only a few cases in their careers. But her team’s AI was able to spot subtle water-diffusion patterns in MRIs—features invisible to the human eye—that reliably signaled tumor infiltration.

“The AI was picking up on things we couldn’t,” Yeom explained. “It gave us early diagnostic certainty in cases where delay could be devastating.”

At Boston Children’s Hospital, Dr. Tina Young Poussaint began applying these tools to complex pediatric cases. The result: diagnostic uncertainty fell by 60%. For parents facing terrifying questions about their child’s future, that level of clarity was priceless.

AI’s Expanding Reach: From Skin to Colon

The success of AI in traditional pathology has sparked innovations across multiple specialties:

Melanoma Detection: By 2024, AI systems analyzing dermoscopic images achieved remarkable accuracy in melanoma detection. Dr. Kamran Avanaki’s OCT-based algorithm at the University of Illinois Chicago could differentiate melanoma from benign nevi with 99% accuracy. In a Stanford Medicine study, AI assistance improved diagnostic sensitivity and specificity across all practitioner levels—medical students improved by 13 points in sensitivity, while even experienced dermatologists saw measurable gains.

Colonoscopy Revolution: Computer-aided detection (CADe) systems have transformed polyp detection during colonoscopy. A 2024 meta-analysis of 44 randomized controlled trials involving nearly 35,000 patients showed that AI-assisted colonoscopy increased adenoma detection rates by 20% and decreased miss rates by 55%. At Yale’s West Haven VA, the implementation of CADe technology has become routine, with endoscopists noting particular value in detecting diminutive polyps that might otherwise be overlooked.

However, these advances come with nuanced challenges. A 2025 Polish study found that continuous exposure to AI assistance might lead to “deskilling”—where endoscopists’ performance without AI actually decreased over time, suggesting the need for balanced training approaches.

Beyond diagnosis

These advances go further than simple yes/no calls. At Stanford, Dr. Michelle Monje, a pediatric neuro-oncologist, now uses AI-driven maps to define the exact borders of brain tumors. Neurosurgeon Dr. Samuel Cheshier describes it as having a “GPS for the brain.” Instead of relying only on what the surgeon can see, AI maps out tumor tissue versus healthy brain in three dimensions. The result: more complete resections, fewer complications, and better outcomes.

Quantitative continuous scoring: the next frontier

At ASCO 2025, AstraZeneca and Daiichi Sankyo presented their Quantitative Continuous Scoring (QCS) computational pathology solution—the first AI-based system to receive FDA Breakthrough Device Designation as a cancer companion diagnostic. The VENTANA TROP2 RxDx assay demonstrated how AI could not just detect cancer, but quantify biomarker expression levels with unprecedented precision, directly informing treatment decisions for targeted therapies.

Meanwhile, in Boston, Dana-Farber Cancer Institute pathologist Dr. Catherine Wu has begun using AI to design personalized cancer vaccines. These vaccines rely on “neoantigens”—mutations unique to each tumor. Identifying them used to take months of laborious work. Now, AI can scan a patient’s genome, flag the best neoantigen candidates, and even simulate how T-cells would recognize them. Early melanoma trials show some patients achieving years of durable remission.

Microsatellite instability: AI sees the invisible

A 2025 breakthrough from Zhengzhou University demonstrated AI’s ability to predict microsatellite instability (MSI) directly from H&E-stained slides in gastroesophageal junction adenocarcinomas—a feat impossible for human pathologists. This matters immensely: MSI status determines eligibility for immunotherapy, but traditional testing requires additional tissue and time. The AI model achieved accuracy rates that could transform treatment timelines for thousands of patients.

AI Strengths and Weaknesses

• AI’s Strength: In narrow diagnostic tasks like reading slides,

CT scans, or mammograms, AI already outperforms human experts in accuracy, consistency, and speed. That’s not future speculation — it’s demonstrated repeatedly in benchmarks and clinical trials.

• The Limitation: AI doesn’t “know” the patient. It doesn’t

integrate medical history, lab results, family risk factors, or personal treatment goals. It analyzes inputs, but it doesn’t deliver holistic care.

Where humans still matter:

• Integration: A doctor decides how to combine the AI’s findings with other clinical details.

• Communication: Explaining results, discussing options.

• Accountability: For now, only licensed professionals can legally sign off on treatment decisions.

AI is also extending beyond glass slides. It is now integrated into workflows that combine pathology, radiology, and liquid biopsy signals into unified dashboards. The next frontier isn’t siloed assistance, but whole-patient modeling—where every pixel, every sequence, every cell fragment contributes to a single, integrated clinical picture.

The market responds: explosive growth

The digital pathology market reflects this transformation. In 2025, equipment sales are projected to capture 56.3% of the market revenue, driven by whole-slide scanners and automated imaging systems from companies like Philips, Leica Biosystems, and Roche. The U.S. market alone is expected to grow at 12% annually through 2035, fueled by over 2 million new cancer diagnoses yearly and chronic pathologist shortages.

Major pharmaceutical companies are investing heavily. Johnson & Johnson’s MIA:BLC-FGFR algorithm can detect FGFR+ mutations in bladder cancer from standard H&E slides in minutes—bypassing the need for expensive molecular testing. This democratization of advanced diagnostics could be particularly transformative in resource-limited settings.

For the borderline cases—the moments where human error is most dangerous—AI has already proven itself superior. It catches what human eyes, especially tired ones, miss. It standardizes judgments that would otherwise vary from one pathologist to another. And it frees specialists to focus on confirmation and oversight rather than first-line detection.

The training revolution

Medical education is adapting rapidly. At the 2025 Digital Pathology Congress in London, educators presented new curricula integrating AI from day one of pathology training. Rather than viewing AI as a threat, the next generation of pathologists is learning to work symbiotically with these tools. As one resident noted, “It’s like having the world’s best attending physician looking over your shoulder 24/7.”

What current trends indicate

• The next five years: AI as decision support. Humans sign off on diagnoses, but AI quietly outperforms them on accuracy.

• Medium term (5-10 years): AI does the bulk of reading, with humans validating edge cases or communicating results. The ratio shifts — far less human time per case.

• Long term (10+ years): In some areas (like screening mammography), AI may operate nearly autonomously, with humans involved only in oversight, legal responsibility, or unusual exceptions.

The human element: enhanced, not replaced

Dr. Douglas Flora, Editor-in-Chief of AI in Precision Oncology, offers a compelling analogy: “Remember when GPS first arrived in our cars? The moment when ‘turn left in 500 feet’ replaced unfolding wrinkled maps and arguing over directions? We are experiencing a similar transformation in oncology.”

This doesn’t mean AI is perfect. It still has an error rate. But crucially, that rate is lower than that of humans. The safest system right now is therefore not human-alone or AI-alone, but AI first, with a human double-check. That flips the traditional order, but it’s the reality of where the technology stands today.

These advances, however, are not yet widely available. The software itself is relatively inexpensive and works with existing imaging systems, but adoption is slowed by other factors: hospitals must adopt it despite the financial incentives of the current processes (advanced cancers are major revenue generators), physicians are reluctant to switch to technology that does what they do better and at a significantly lower cost, and reimbursement systems lag behind innovation. As a result, access today is concentrated in large academic and research centers, while other hospitals, even larger urban hospitals, are slow to catch up.

For patients, the new technologies will be adopted sooner or later and mean that cancers are caught when they are most easily treated. Fewer will be missed and fewer unnecessary procedures will be imposed upon misdiagnosed patients. And fewer lives will be unnecessarily lost.

. . .

Our new book, “Surviving Cancer: Hope based on emerging medical science” is now live on Amazon and available to order here.

If you or a loved one is dealing with a diagnosis that you would like to learn more about, you can order the Leading Edge report on the latest medical science, customized to that specific diagnosis, here.

Email me at Emerging Cures if you’d like to talk, rod@emergingcures.org.

You can read about my own cancer journey (My History With Terminal Cancer) here.

The book launched a few minutes ago. It contains everything I’m currently aware of in terms of emerging medical science for cancer treatment.

A major objective of this book is to pressure hospitals, physicians and insurers to make these new life-saving technologies available to those who would benefit most from them. If you order the book, it would help me achieve that objective if you would leave a review. You can order the Kindle version of the book, and leave a review, here:

https://amzn.to/48EKOlN

And thank you!

Summary:

The medical science driving new cancer treatments is evolving at a breathtaking speed. What seemed like science fiction just five years ago now saves lives daily in treatment centers across the country. Yet this revolution remains frustratingly uneven—available to some, unknown to many, inaccessible to others. Understanding these advances and how to access them can determine quality of life issues for patients, and even life itself.

• MIRAI, an AI platform developed by MIT’s Regina Barzilay, detects breast cancer years before current methods. It has been validated on millions of mammograms across dozens of hospitals, in multiple countries, and with diverse populations. Once deployed it is inexpensive. And yet hospitals won’t use it.

• Rather than the standard-of-care under which two people with the same diagnosis respond in dramatically different ways, one living, the other dying, the new medical science is able to custom design drugs to an individual patient’s disease and genetic characteristics.

• Computers are able to design a patient’s digital twin, and in a simulation test determine the likely outcome of a particular therapy on a patient before treatment.

• Liquid biopsies, powered by AI, are detecting cancer and planning next steps in treatments from blood samples.

• Foundation models like Stanford’s MUSK are integrating images, pathology, and genomic data to accurately predict outcomes.

• Cell therapies like CAR-T and CAR-NK are teaching immune systems to fight cancers once deemed untreatable.

• Revolutionary treatments are being developed such as that by the Korea Advanced Institute of Science and Technology which coax cancer cells into becoming normal tissue again.

For decades, progress against cancer depended on slow laboratory experiments, chance discoveries, and treatments that often harmed healthy cells as much as tumors. Today, artificial intelligence, quantum computing, and advanced simulations are turning medical research upside down.

To learn more about the work of Emerging Cures, visit www.emergingcures.org.

AI is transforming medicine. Nowhere is this more apparent than in radiology where AI can do what physicians do faster, cheaper and better. Next will be pathology, for the same reasons. We still need radiologists and pathologists, but in much fewer numbers. The transition is slowed by hospitals and physicians resisting the change because it is threatening the economic model, but it must happen because the new technologies are life-saving.

AI is now demonstrably more accurate, faster and cheaper than human radiologists. It’s not perfect — it has an error rate — but that rate is significantly lower than that of human radiologists. We still need humans to check over the results generated by AI, but we need only a small fraction of the radiologists now employed. As you can imagine, these physicians, despite the huge advantages of AI to patients, are fighting this tooth and nail. Next, after radiologists, the need for pathologists will be significantly reduced. We’ll need some, again, to confirm the results generated by AI. During this transition, the powers that be will do everything they can to slow the process down.

The human eye and brain are subject to fatigue. Training biases them. Expertise sharpens some edges and blunts others. AI never tires. It is capable of sifting through millions of pixels and billions of data points to see what no physician can. And every day AI benefits from retraining, from the feedback loop as it is fed the results of its diagnosis, accurate and inaccurate. Every day its reliability improves a little; every day its advantage over humans widens.

As Dr. Curtis Langlotz at Stanford University explains, artificial intelligence is changing everything in diagnosis.

“I remember the exact moment I realized AI would change everything,” Langlotz recalls. “I was reviewing chest X-rays late one evening when our experimental AI system flagged a subtle nodule I had missed.”

Few cancers illustrate the stakes more starkly than lung cancer.

Lung cancer remains the leading cause of cancer death worldwide, in large part because it is usually diagnosed too late. Small nodules on CT scans may or may not be malignant. Radiologists often must decide whether to dismiss, monitor, or biopsy — and their error rate costs lives.

At Google Health, Dr. Shravya Shetty and her team took on this challenge. Working with the National Cancer Institute and major medical centers, they developed an AI system that analyzes not just individual CT slices but the entire three-dimensional structure of the lungs. The system also tracks changes over time, learning the subtle textures and growth patterns invisible to the human eye.

Dr. Michael Gould, a pulmonologist at Kaiser Permanente who collaborated on the project, was astonished:

“The AI system could predict which small nodules would become cancerous with 94% accuracy. That’s better than most experienced thoracic radiologists.”

The bigger story came when the model was put into practice. At Intermountain Healthcare, thoracic surgeon Dr. Thomas Varghese oversaw deployment across the network. “We went from missing 20% of early-stage cancers to missing less than 5%,” he reported. “That translates to hundreds of lives saved each year in our system alone.”

Case studies from the front lines

Across the country, AI is proving itself but is not being adopted as standard practice:

• Chicago, lung cancer screening : Radiologists reviewing low-dose CT scans used an AI system that automatically ranked nodules by malignancy risk. The AI didn’t replace biopsy or follow-up imaging, but it made sure no suspicious lesion got lost in the shuffle.

• These AI-based systems for lung nodule detection, characterization, triage, and malignancy risk have been validated in clinical trials. An example: clinical trial NCT07052773 (“Clinical Evaluation of the Lung Cancer AI-based Decision Support”). Nevertheless, they are not being used or incorporated into standard practice.

• One company, Aidence, offers “Veye Lung Nodules,” a diagnosis tool used in some European hospitals but not adopted in the US.

• AI-digital pathology to identify borderline prostate cancer cases:  AI overlays helped pathologists resolve borderline prostate cancer cases. In multi-reader trials, accuracy improved, turnaround time dropped, and consistency rose. In this case, the digital pathology overlays are closer to real-world adoption in some labs, though not in most hospitals.

• Paige Prostate received FDA authorization in 2021 and is in use in a few locations in the US. Use requires infrastructure (digital pathology scanners, slide digitization, image management systems). Many labs still rely on glass-slide-microscope workflows, which is a barrier to broad adoption. To the extent that investment in digital capability requires that radiologists submit budget requests to their hospitals, they are likely reluctant to do that since the technology replaces a substantial portion of their jobs.

• NYU Langone launched a digital pathology program across its network.

• Ibex Prostate Detect is being deployed in UTMB; Alverno Labs, a large Midwest network. These systems create heatmaps/alerts to aid routine reads and are being deployed in clinical workflows.

Bottom line: FDA approved radiology is being implemented in a few hospitals, including in New York, (e.g., NYU’s digital pathology infrastructure; Paige’s MSK roots; Mount Sinai’s AI pathology research and deployments) but is not being widely deployed, despite its clear advantages in accuracy, efficiency and cost. Said another way, these technologies replace most, though certainly not all, human radiologists and are thus not adopted.

Seeing beyond the human eye

The new AI-assisted tools are especially useful in pediatric oncology, where many doctors see only a handful of rare cancers in their entire careers. Dr. Kristen Yeom, a pediatric radiologist at Stanford, faced this challenge in diagnosing brain tumors in children.

With colleagues, she helped build an AI system that analyzed pediatric brain MRIs. At first, it simply confirmed what experts already knew. Then it began identifying features even experienced neuroradiologists had never recognized.

“The AI was picking up on subtle patterns in how water molecules moved through brain tissue,” Yeom recalled. “These were invisible to human eyes but turned out to be early indicators of tumor infiltration.”

At Boston Children’s Hospital, Dr. Tina Young Poussaint put the system into clinical use. “We reduced our diagnostic uncertainty by 60%,” she said. “For parents facing their child’s potential cancer diagnosis, that certainty is invaluable.”

The benefits extended to treatment planning. Dr. Michelle Monje, a pediatric neuro-oncologist at Stanford, used the AI to map tumor boundaries, enabling surgeons to remove malignant tissue while preserving healthy brain function. Dr. Samuel Cheshier, a pediatric neurosurgeon, described the change simply:

“AI has become our GPS for navigating the brain.”

Decoding the genome’s hidden language

Patterns aren’t confined to images. Cancer also hides its signals deep within the genome. In the early 2000s, Dr. Victor Velculescu at Johns Hopkins recalled feeling overwhelmed after sequencing cancer genomes.

“We had all this genetic data, but it was like having a library in a foreign language.”

At the Broad Institute, Dr. Gad Getz built AI algorithms that could read this hidden language. His lab trained models on tens of thousands of patient genomes to distinguish driver mutations from harmless passengers. Collaborating with Dr. Shamil Sunyaev at Brigham and Women’s, they pushed prediction accuracy for harmful variants to above 90% and reduced false positives up to 20% in some settings.

This marked a leap from studying individual genes to modeling entire networks of genetic interactions. What once took years of painstaking analysis now emerged in weeks, even days, giving oncologists insight into which mutations truly mattered.

CheXNet — the Stanford breakthrough

Stanford’s AI lab, led first by Dr. Andrew Ng and later Dr. Pranav Rajpurkar, developed one of the first landmark systems: CheXNet, a deep learning model that could identify 14 different pathologies in chest X-rays with accuracy on par with expert radiologists.

The real breakthrough was not only diagnostic accuracy  —  it was speed and consistency. It takes AI seconds to read a CT scan or MRI; it might take a radiologist five minutes or more. And, as Dr. Matthew Lungren, a radiologist and AI researcher at Stanford, explained:

“A radiologist might read 100 chest X-rays in a day and perform at 95% accuracy when fresh, but that accuracy drops with fatigue. AI never gets tired, never has a bad day, and can process thousands of images with unwavering precision.”

Radiology images are digital, high-volume, and standardized  —  ideal fuel for AI training. That has made radiology one of medicine’s earliest proving grounds for AI. And some say the first victim.

CHIEF  —  A universal cancer detector

If radiology was AI’s first proving ground, pathology may be its most ambitious. Pathologists spend hours analyzing tissue under microscopes  —  a process that is time-consuming and subjective. With the digitization of pathology slides, AI is transforming this field.

In 2025, researchers at Harvard Medical School introduced CHIEF (Cancer Histology Image Evaluation Foundation), trained on 44 terabytes of pathology data across millions of slides. The results were remarkable:

• 96% accuracy across 19 cancer types.

• Ability to predict survival outcomes directly from pathology images.

• Identification of genetic mutations without costly genetic testing.

Chamath Palihapitiya, a well-known venture capitalist, called it a paradigm shift, “Through more efficient evaluations, CHIEF could enable better diagnosis and treatment.” Dr. Pranav Rajpurkar, involved in its development, emphasized its robustness: CHIEF was validated on 32 datasets from 24 hospitals, overcoming one of the biggest challenges of AI  —  failing outside the lab where it was built.

Tissue slides hold more than meets the eye. AI can detect hidden patterns  —  clues to prognosis or mutations  —  that even the most experienced pathologists cannot see.

Large language models

AI’s promise extends beyond images. In 2024, a randomized clinical trial published in JAMA Network Open compared physicians’ diagnostic reasoning with and without access to a commercially available large language model (LLM).

• On its own, the LLM scored a median of 92% per case  —  outperforming physicians.

• Yet physicians paired with the LLM did not improve significantly compared to those working unaided. They scored 83%. Physicians alone scored 80%.

The paradox was striking: the AI alone outperformed both groups of humans, but simply giving it to doctors didn’t help. Researchers suggested two reasons:

• Physicians were not trained in how to interact effectively with AI.

• AI needs to be integrated into workflows. It won’t happen on its own. Producing a better answer then requires humans to pay attention and incorporate it into treatment regimens.

In addition to skill level  —  AI can see things that humans can’t, and AI doesn’t experience fatigue  —  another issue is human bias. Humans, protective of skills they’ve put years into developing, dismiss AI as unnecessary or error-prone despite evidence to the contrary. A study grounded in the Dunning–Kruger Effect found that individuals who overrate their own performance are less likely to rely on AI, hindering optimal collaboration. (Cornell study: Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems).

There are also legal and ethical concerns that shape skepticism. For instance, when AI flags an abnormality missed by a radiologist, it could increase perceived liability or “AI penalty”, so professionals may unconsciously emphasize AI’s limitations to protect themselves. Medical malpractice suits may face more complex challenges with the rise of artificial intelligence, according to a study led by Brown researchers that suggested that radiologists are viewed as more culpable if they fail to find an abnormality detected by AI. (See: Use of AI complicates legal liabilities for radiologists, study finds. July 28, 2025). In other words, among radiologists and pathologists, AI is widely considered to be a threat. It is.

AI Is more predictive than humans by a wide margin

The advantage of AI is particularly profound when it comes to predicting months or years in advance which patients will get cancer.

1. Mammography-Based AI (2–6 years ahead)

• The MIRAI model predicts the risk of developing breast cancer up to five years in advance  —  demonstrating strong predictive accuracy and cross-population validation. Massachusetts General Hospital NAM

• Another study using commercial AI algorithms (INSIGHT MMG) found that AI scores were significantly higher 4–6 years before clinical detection, with AUC values climbing over time  —  showing meaningful lead time for early intervention. JAMA Network News-Medical

2. Digital Breast Tomosynthesis (5-Year Forecast)

A deep learning model using digital breast tomosynthesis (DBT) forecasts an individual’s 5-year breast cancer risk. It is based on an AI system that analyzes ultrasound images with an accuracy rate of 80%, significantly higher than human pathaologists.

3. Liquid Biopsy and Circulating DNA (Months to Years ahead)

Technologies like Guardant’s Reveal, Natera’s Signatera, and others detect cancer relapse between ~8 months to over 28 months earlier than traditional imaging.

4. HPV-Related Head and Neck Cancers (Up to ~10 years ahead)

The HPV-DeepSeek test identifies HPV-associated head and neck cancers up to 8–10 years before symptoms or clinical diagnosis by detecting tumor DNA fragments via machine learning-enhanced blood testing.

5. Blood Protein Biomarkers (7+ Years ahead)

A study from the UK Biobank found that certain blood proteins correlated with a cancer diagnosis over seven years later, suggesting long-term early warning potential.

PanSeer, a noninvasive liquid biopsy, detected several cancer types in 95% of asymptomatic individuals who were later diagnosed  —  even up to four years before standard diagnosis. This extended lead time opens new possibilities for earlier intervention, targeted preventive screening, and meaningful improvements in patient outcomes.

Self-diagnosis with AI

Not all medical AI flows through hospitals. Increasingly, patients are using AI tools directly. Some stories are controversial, but others are lifesaving.

Lauren Bannon, dismissed with “acid reflux,” asked ChatGPT about her symptoms in 2025. It suggested Hashimoto’s thyroiditis, prompting further tests that revealed a neck tumor. “AI saved my life,” she said.

Marly Garnreiter had unexplained symptoms that stumped specialists until AI flagged Hodgkin lymphoma  —  confirmed months later.

Sam West used AI-driven statistics to identify a rare tumor variant six months before experts recognized it.

Robert Wright, after noticing a neck lump, turned to AI for guidance. It urged him to seek care, revealing throat cancer. Now cancer-free after immunotherapy, he hosts a podcast on AI in health.

These cases highlight both AI’s democratizing power and its risks. While AI can push patients toward timely care, it can also mislead, over-reassure, or spark unnecessary anxiety.

A note of caution: I am concerned that people with a life-threatening condition might conclude from my work that physicians and hospitals are best avoided. Don’t do that. It will likely end in disaster. Use AI to choose which hospital or clinic to go to for treatment, to prepare questions prior to doctor visits, and to consider, with your doctor, which treatments to avoid based on quality-of-life considerations as well as longevity. AI can be an invaluable, life-saving tool, but doctors and hospitals are crucial to your recovery.

Conclusion

• Across radiology, pathology, diagnostic reasoning, and even patient self-advocacy, AI is establishing itself as better at diagnosis than traditional radiology. Traditional diagnostics still have a role to play, but a diminished one. As patients, we don’t want to accept an AI-generated diagnosis and proceed with surgery, for instance, without a human expert reviewing the AI’s conclusion.

• In radiology, tools like CheXNet read images quickly and consistently.

• In pathology, CHIEF shows the potential of universal multi-cancer detection.

• In clinical reasoning, LLMs show the potential for enormous contributions to patient survival. They are not being widely used.

• For patients, AI empowers  —  but has major limitations. As patients, we should not conclude that physicians no longer have a role to play. They do. An important role.

• Though many of these new technologies have been repeatedly validated, clinical adoption is slow – much slower than it should be. Physicians are reluctant to adopt technologies that make their years of training obsolete.

. . .

You can pre-order the new book, “Surviving Cancer: Hope based on emerging medical science”

, to be released on Amazon later this week, at the discounted pre-order price of $14.95 here. After publication, the price will be $19.95. You can also pre-order the Kindle version ($6.99) at that same link.

If you or a loved one is dealing with a diagnosis that you would like to learn more about, you can order the Leading Edge report on the latest medical science, customized to that specific diagnosis, here.

Email me at Emerging Cures if you’d like to talk, rod@emergingcures.org.

You can read about my own cancer journey (My History With Terminal Cancer) here.

When Dr. Kunle Odunsi first began treating ovarian cancer patients decades ago, he was haunted by a sobering reality: more than 70 percent would initially respond to treatment, sometimes achieving complete remission. He would tell these patients there was no evidence of disease, and sometimes they would hug him. But at the back of his mind was the knowledge that roughly 70 percent would relapse, often with devastating consequences.

"That became one of the driving forces for me initially," recalls Dr. Odunsi, who now directs the University of Chicago Comprehensive Cancer Center and holds membership in the National Academy of Medicine. "I began to ask several questions: How can we extend remission rates in ovarian cancer patients? Can we use the immune system to prevent relapse of cancer, similar to how we use vaccines for seasonal flu, when a patient is in remission?"

Today, Dr. Odunsi leads a groundbreaking $15 million collaboration with Argonne National Laboratory, using artificial intelligence to revolutionize cancer drug discovery. This partnership exemplifies the profound transformation occurring in cancer medicine. Medical science is changing at a phenomenal rate, with AI serving as the primary driver of this revolution. The new drugs, surgical procedures, and diagnostic processes emerging from AI-powered research are vastly superior to those they are replacing. For anyone facing a life-threatening disease, understanding these advancements could mean the difference between a terminal diagnosis and effective treatment.

Predicting Whether a Patient's Cancer Will Respond to Immunotherapy

At the forefront of this revolution, pioneering physicians are developing AI tools that fundamentally change how cancer is detected, understood, and treated. Dr. Eytan Ruppin, who leads the Cancer Data Science Laboratory at the National Cancer Institute, exemplifies this new generation of physician-scientists combining clinical expertise with computational power.

Dr. Ruppin and his collaborator Dr. Luc Morris at Memorial Sloan Kettering Cancer Center recently developed LORIS (Logistic Regression-Based Immunotherapy-Response Score), an AI tool that uses routine clinical data to predict whether a patient's cancer will respond to immunotherapy. The tool analyzes just six variables commonly measured in clinical settings—including the patient's age, cancer type, blood albumin levels, and neutrophil-to-lymphocyte ratio—to calculate a score indicating response likelihood.

"We were able to develop a new predictive model for immunotherapy response across many different cancer types using only six simple variables," Dr. Morris explains. "In contrast to prior models, some of which are very complex, this model is very accessible to clinicians."

Meanwhile, at Harvard Medical School, Dr. Kun-Hsing Yu has developed CHIEF (Clinical Histopathology Imaging Evaluation Foundation), a ChatGPT-like AI platform that performs multiple cancer evaluation tasks simultaneously. "Our ambition was to create a nimble, versatile ChatGPT-like AI platform that can perform a broad range of cancer evaluation tasks," Dr. Yu explains. "Our model turned out to be very useful across multiple tasks related to cancer detection, prognosis, and treatment response across multiple cancers."

CHIEF successfully predicted patient survival based on tumor histopathology images obtained at the time of initial diagnosis. In all cancer types and all patient groups under study, CHIEF distinguished patients with longer-term survival from those with shorter-term survival, outperforming other models by 8 percent overall and by 10 percent in patients with more advanced cancers.

AI's Diagnostic Revolution

These physician-led advances are delivering unprecedented diagnostic accuracy. AI systems now surpass human diagnostic precision across multiple cancer types. FDA-cleared systems reach 98.7% sensitivity for stroke detection. AI software trained on large datasets of brain scans has proven "twice as accurate" as human professionals in interpreting CT scans, MRIs, and X-rays.

In surgical interventions, AI-assisted robotic procedures reduce operative times by 25% and intra-operative complications by 30%, while improving surgical precision by 40%. AI-powered tools are revolutionizing the detection of musculoskeletal injuries, spotting more bone fractures than humans—crucial given that urgent care doctors reportedly miss broken bones in up to 10% of cases.

The technology extends far beyond imaging. In pilot programs, AI triage systems reduce missed incidental findings in abdominal CTs by over 60%. An AI tool has successfully detected 64% of epilepsy brain lesions that radiologists had previously missed. Most remarkably, AI can detect diseases years in advance with high confidence for over 1,000 conditions, often before symptoms manifest clinically—identifying acute kidney injury risk up to 48 hours before clinical signs appear.

The Precision Medicine Revolution

A primary characteristic of the new AI-driven therapies is drugs custom-designed to an individual patient's disease and genetic characteristics. Advanced treatments now target specific diseases based on precise molecular targets rather than applying broad chemotherapy or radiation that might work for one person but fail for another with the same diagnosis.

AI assesses both the cancer's genetic makeup and the individual patient's genetics, maximizing drug effectiveness while minimizing adverse reactions. Integrated with wearable devices, AI can continuously monitor patient health, detect abnormalities, and even adjust dosages automatically.

One targeted therapy category, monoclonal antibodies, represented 22% of FDA approvals in 2023. These laboratory-designed proteins stimulate our immune system to treat cancer, autoimmune diseases, and infections like COVID-19. These "designer antibodies" are highly specific, binding to single targets for precise immune responses.

In another breakthrough, AI recently enabled the design of new "binders" that can attach to shape-shifting amorphous proteins for the first time, including some implicated in cancer and Alzheimer's. Previously, designing such binders took drug companies months or years. A new AI tool from the University of Washington can design binders for many previously "undruggable" proteins.

This AI software recognizes disordered proteins and generates binders by understanding the target's overall shape, recombining binding pockets in different configurations to create libraries of templates. Using diffusion AI techniques, it generates roughly a thousand pockets allowing for trillions of combinations that can grab onto these challenging proteins.

Global Treatment Advances

Important developments emerge from research centers worldwide. In 2024, the FDA approved 50 novel drugs alongside nine new cellular and gene therapy products, totaling 59 new medical therapies. In Europe, this number reached 114 new medicines, including 46 novel compounds.

In October 2024, researchers announced the biggest breakthrough in cervical cancer treatment in two decades through the INTERLACE trial conducted at 32 medical centers across Brazil, India, Italy, Mexico, and the UK. Results showed that giving cervical cancer patients a short course of chemotherapy before standard treatment reduced death risk by 40% and reduced cancer recurrence risk by 35%.

Some breakthrough drugs don't complete the FDA approval process due to funding limitations rather than efficacy concerns. The pharmaceutical industry spends an estimated $2 billion on average to bring a single drug to market. Companies behind worthwhile drugs sometimes exhaust resources trying to meet U.S. regulatory requirements. At Emerging Cures, we actively seek drugs championed by respected physicians and backed by strong data that didn't complete approval due to funding constraints rather than safety or efficacy issues.

Toward Prognostication

Pattern recognition is only the beginning. Once AI learns to see “what is there,” it can begin to predict “what will happen.” Mayo Clinic researchers recently demonstrated AI's predictive power by examining medical records of pancreatic cancer patients, analyzing abdominal CT scans and blood tests taken up to three years before diagnosis. Using advanced algorithms measuring changes in body fat, muscle, bone, and blood biomarkers like cholesterol and glucose, they identified subtle yet significant body changes occurring as pancreatic cancer develops. In 2023, these models were tested against millions of patient health records. By spotting obscure patterns in disease codes — many not directly tied to the pancreas — the system outperformed population-based estimates and even some genetic sequencing tests.

At Stanford Medicine, researchers developed MUSK (Multimodal Transformer with Unified Mask Modeling), an AI model combining visual and text data to predict cancer outcomes with remarkable accuracy. Trained on 50 million pathology slides and a billion pathology-related texts, MUSK integrated visual and textual data to predict prognosis across 16 cancers. In validation, it correctly forecast disease-specific survival 75% of the time, compared with 64% for conventional staging.

For non-small cell lung cancer, MUSK correctly identified patients who benefited from immunotherapy treatment about 77% of the time, while the standard method based on PD-L1 expression was correct only about 61% of the time.

Looking Forward: The Next Twelve Months

When I asked an AI platform about cancer medicine's trajectory over the next six to twelve months, the response revealed the extraordinary pace of change ahead. By mid-2026, AI's presence in cancer research will shift from innovative tool to indispensable partner, driving increasingly precise, personalized, and rapid advancements.

More AI-powered diagnostic and prognostic tools will gain regulatory approvals and transition from research to widespread clinical use. The European Federation for Cancer Images (EUCAIM) project aims to establish pan-European digital infrastructure for cancer images with at least 50 AI tools by 2026.

AI models will become more sophisticated at integrating vast, diverse datasets—combining genomics, proteomics, metabolomics, spatial transcriptomics, and high-resolution 3D imaging data. This will provide unprecedented insights into tumor microenvironments and immune responses, leading to more effective targeted therapies and immunotherapies.

Multi-cancer early detection tests will see further refinements in sensitivity and specificity, potentially leading to additional regulatory breakthroughs and expanded trials for population-wide screening. AI will play critical roles in distinguishing true cancer signals from background noise, improving test reliability.

Generative AI will move beyond predicting molecule properties to autonomously designing and optimizing entirely novel compounds with specific desired characteristics. This will further shorten drug discovery timelines and reduce R&D costs, with higher probabilities of successful candidates entering clinical stages.

AI-driven adaptive clinical trials will become standard practice, using real-time adaptation to emerging safety and efficacy signals for faster treatment evaluation. AI's role in patient stratification, outcome prediction, and trial automation will become central, making trials more efficient, cost-effective, and inclusive.

There will be increasing emphasis on developing interpretable and "explainable AI" models, allowing researchers and clinicians to understand how AI makes predictions. This will foster greater trust, address ethical concerns around bias, and facilitate regulatory approval and widespread adoption.

The Bottom Line for Patients

AI is dramatically accelerating drug discovery, enhancing diagnostic accuracy, enabling personalized treatments, and boosting medical research efficiency. The transformation is happening now, not in some distant future. Physicians like Dr. Ruppin, Dr. Odunsi, Dr. Kontos, and Dr. Litzow are leading this revolution, developing tools that can identify treatment opportunities traditional approaches might miss.

"I believe this 'dream team' has the potential to revolutionize the cancer drug discovery timeline and change the paradigm for patients that currently have a poor prognosis and little hope for recovery," Dr. Odunsi reflects. The revolution is underway, and it might save your life.

If you face a life-threatening disease, you need to understand what's happening in clinical trials and emerging therapies relevant to your condition. The AI-driven advances emerging from leading research centers worldwide may offer options that weren't available even months ago. Tools like LORIS are already publicly available at

https://loris.ccr.cancer.gov

for assessing immunotherapy response likelihood. These tools are life-saving, but unfortunately not yet widely deployed.

“Validated across millions of scans, Regina Barzilay AI-diagnosis tool could transform medicine — yet hospitals and insurers still won’t let patients benefit. Radiologists haven’t exactly embraced it either.”

Cambridge, Massachusetts – The Personal Catalyst

In 2014, MIT computer scientist Regina Barzilay received a diagnosis that would transform not just her life, but potentially millions of others: breast cancer. As she sat through chemotherapy treatments, this master of natural language processing—who had spent years teaching computers to decode complex patterns in text—confronted a haunting question: What if the signals of her cancer had been there all along, hidden in her mammograms from years before, but neither she nor her doctors had the tools to read them?

"Right now, we wait for the tumor to appear and then we pounce," she would later say. "That is a failure. We need to see the future. We need to prevent the fire, not just be excellent firefighters."

The Ghost in the Machine

Standard mammogram analysis, Barzilay realized, was tragically simplistic. Radiologists looked for obvious signs—masses, calcifications—but she believed cancer's story was written in subtler ink. It lived in the texture, density, and vascular patterns of surrounding tissue, what she called a "suspicious background" that the human eye dismissed as normal.

Partnering with Massachusetts General Hospital and Dr. Constance Lehman, chief of breast imaging, Barzilay assembled a team including her then-student Adam Yala. They gained access to a treasure trove: over 90,000 mammograms initially, then hundreds of thousands more, from patients whose outcomes were already known.

The approach was elegantly simple yet revolutionary. They didn't tell the AI what to look for. They simply showed it mammograms from women who later developed cancer and those who didn't, letting the machine discover the differences itself—a classic machine learning problem of finding signal in the noise.

What emerged was remarkable. The AI began identifying patterns so subtle, so deeply embedded in the breast tissue, that they were literally invisible to human radiologists. When veteran radiologists examined the areas the AI flagged as high-risk, their responses were telling:

"I don't see anything." "It's just normal dense tissue."

The AI was seeing a ghost—a diffuse signature woven through the breast tissue that appeared years before any tumor would form. The implications were profound. Instead of a one-size-fits-all annual mammogram, screening could become personalized and proactive. Her AI model wasn't a diagnostic tool; it was a prophecy engine.

Barzilay's journey from computer scientist to cancer researcher exemplifies the convergence happening across medicine today. Out of her early work, four major projects have evolved that are changing medicine:

Mirai: The Future-Telling System

In Japanese, Mirai means "future." Unlike traditional risk models like Tyrer-Cuzick that rely primarily on family history and achieve accuracy rates around 62%, Mirai examines the mammogram itself, finding patterns in:

• Tissue density variations

• Architectural distortions invisible to the human eye

• Subtle textural features

• Glandular structures that correlate with future disease

The underlying principle of Mirai is its capacity to discern subtle, a-human-eye-cannot-perceive patterns in the breast tissue as captured by the mammogram image. Dr. Barzilay's team determined that "the tissue itself imprints a lot of information," containing subtle cues that correlate highly with future cancer development. This is not merely a technical achievement but a reframing of medical diagnostics. The model is not just performing pattern recognition on existing pathologies; it is revealing latent biological processes that are in place to enable cancerous growth long before a tumor is visible. This shifts the paradigm from simple diagnosis to predictive pathology, enabling physicians to move from reactive treatment to proactive, preventative care.

The model predicts the development of cancer 1, 2, 3, 4, and 5 years into the future. This multi-time-point prediction enables truly personalized screening schedules.

What the AI Tools Have Proven

• MIRAI has been validated on millions of mammograms across dozens of hospitals, in multiple countries, and with diverse populations.

• In head-to-head comparisons, MIRAI consistently outperforms traditional risk models like Tyrer–Cuzick — the only formally recognized and widely available clinical approach — achieves about 62% accuracy. MIRAI, validated across millions of scans, reaches concordance indices of 0.75 to 0.84, a major leap in predictive power.

• Cost? Essentially zero per patient once deployed. Meanwhile, radiologists are expensive, late-stage cancer care is ruinously expensive — and patients pay the price for a system that refuses to use better, cheaper tools.

Sybil: Extending AI's Reach to Lung Cancer Detection

Building upon the success of Mirai, Dr. Barzilay’s group developed the Sybil model, a validated deep learning model for lung cancer prediction. Sybil operates on the same core principle as Mirai but applies it to a different cancer type and imaging modality. The model analyzes a single low-dose chest computed tomography (CT) scan to predict the future risk of a patient developing lung cancer.

The creation of Sybil is significant because it demonstrates that the core methodology developed for Mirai—training deep learning models on large, high-quality, outcome-labeled medical image datasets—is a robust and generalizable framework for risk assessment in oncology. The success of these two models indicates that Dr. Barzilay’s work is not a series of isolated breakthroughs but rather a foundational blueprint for tackling a broad spectrum of diseases. The research group's efforts are focused on improving health outcomes for conditions including cancer, blood diseases, organ transplants, and diabetes, suggesting a long-term strategy to apply this AI framework across diverse areas of medicine. The successful application of this methodology to lung cancer confirms its potential to fundamentally transform preventative care by enabling the early detection of numerous conditions that were previously difficult to predict.

A New Era of Antibiotics: The Discovery of Halicin

In the realm of drug discovery, Dr. Barzilay’s recent work has made equally profound impacts, most notably with the discovery of the antibiotic Halicin in collaboration with Professor James Collins. This breakthrough was a direct response to the global crisis of antimicrobial resistance, which has been exacerbated by the traditional, trial-and-error approach to drug development—a process that is both labor-intensive and has a high failure rate.

Dr. Barzilay’s team approached this challenge by training a deep neural network to predict molecules with antibacterial properties. This model was then used to screen millions of chemical compounds in a matter of days, a task that would take human researchers years to accomplish. The model successfully identified Halicin, a molecule that had been previously investigated for diabetes treatment but was not known to possess antibacterial properties. Halicin was found to be structurally divergent from conventional antibiotics, and it exhibited a powerful bactericidal activity against a wide spectrum of pathogens, including some of the most dangerous, drug-resistant bacteria like Acinetobacter baumannii and Mycobacterium tuberculosis.

This discovery holds immense significance for several reasons. Halicin was not a known antibiotic, validating the model’s ability to discover structurally novel compounds. This success indicated that AI can fundamentally change the traditional research by moving beyond analysis to de novo design, or the creation of entirely new solutions. Halicin demonstrated that AI could create novel molecules at a scale and speed that was previously unimaginable. This philosophical and methodological shift from analyzing existing data to creating new solutions is a cornerstone of her most recent molecular discovery work.

Democratizing Drug Design: The Boltz Models

Building on the success of Halicin, Dr. Barzilay’s group has continued to push the boundaries of AI in molecular discovery with the development of the Boltz models. In an industry where powerful biomolecular foundation models like AlphaFold3 are often proprietary and restrictively licensed, her team made a strategic decision to release their models under an open-source MIT license, democratizing access to cutting-edge drug design tools.

The Boltz-1 model was released in November 2024 as an open-source alternative to AlphaFold3 for predicting the 3D structure of biomolecular complexes, including proteins, DNA, and RNA. This was followed by the release of the improved Boltz-2 model in June 2025, which introduced a key new feature: the ability to jointly model molecular structure and predict binding affinity. Binding affinity, which measures the strength of interaction between a molecule and a protein, is a critical factor in determining a drug's potency and has historically been a major bottleneck in the drug development pipeline.

Boltz-2 represents a major technical advancement. It is capable of predicting both structure and binding affinity with an accuracy that approaches the gold standard of physics-based simulations (Free Energy Perturbation), while operating thousands of times faster. The model also incorporates a technique called "Boltz-steering," which forces the model to adhere to the fundamental rules of chemistry, ensuring the physical plausibility of its predictions.

The open-source release of the Boltz models is a strategic move to catalyze the entire field of drug discovery. It removes the significant financial and access barriers that typically hinder the initial "hit discovery" phase of research. By making these powerful tools universally available, Dr. Barzilay's group is not only advancing the technology but also challenging the traditional intellectual property-driven model of pharmaceutical research, potentially accelerating the development of new therapeutics for the entire global community.

Global Validation and Impact

By 2025, MIRAI was validated by over 2 million mammograms in 48 hospitals across 22 countries. The MIT Jameel Clinic announced that MIRAI has been validated on more than 1.5 million mammograms in 43 hospitals across 14 countries.

In rigorous testing across seven diverse hospital systems—Massachusetts General Hospital (USA), Novant (USA), Emory (USA), Maccabi-Assuta (Israel), Karolinska (Sweden), Chang Gung Memorial Hospital (Taiwan), and Barretos (Brazil)—Mirai obtained concordance indices ranging from 0.75 to 0.84, significantly outperforming traditional methods.

The real-world impact has been profound:

For High-Risk Patients: Women flagged by Mirai receive enhanced screening with MRI, genetic counseling, and preventive interventions. In one documented case, a woman whose mammogram appeared completely normal to radiologists was identified as high-risk by Mirai. Enhanced monitoring detected her cancer within two years—while it was still highly treatable.

For Low-Risk Patients: Women identified as low-risk can potentially space out screenings, avoiding unnecessary radiation exposure, anxiety, and expense.

For Equity in Care: Critically, Mirai performs equally well across all racial groups—addressing a crucial healthcare disparity, given that Black women are 43% more likely to die from breast cancer than white women.

Beyond Breast Cancer: Expanding Horizons

Barzilay's work has expanded far beyond mammography. Her team has developed:

VaxSeer: An AI model that estimates the expected effectiveness of candidate flu vaccines by predicting which versions of the flu virus are likely to spread next season. This tool analyzes global patterns of flu evolution to forecast which strains will dominate, potentially revolutionizing how annual flu vaccines are formulated.

Cancer Treatment Response Prediction: Working on models that can predict how individual patients will respond to different cancer therapies, moving toward truly personalized treatment plans.

Molecular Symmetry Methods: Developing theoretical advances that allow AI systems to better handle the repeating and mirrored patterns in molecular structures, making drug discovery models more accurate and efficient.

Recognition and Future Vision

In 2025, Barzilay was awarded the IEEE Frances E. Allen Medal for "innovative work in computing leading to lasting impact on other aspects of engineering, science, technology, or society." She was also named to TIME's list of the 100 most influential people in AI in 2025.

As part of the MATCHMAKERS team selected by Cancer Grand Challenges, Barzilay will receive up to USD 25 million in funding over five years to work on understanding how T cells recognize tumors, potentially revolutionizing immunotherapy.

At UMass Memorial Hospital alone, the team has accrued over 7,400 patients in their prospective trial, with MIRAI showing a cancer detection rate of 33 percent compared to just 6.4 percent using breast density for prediction.

The Personal Mission Continues

For Regina Barzilay, this work remains deeply personal. Having experienced the uncertainty and fear that comes with a cancer diagnosis, she's driven by a vision where no one has to wonder if their cancer could have been caught earlier.

"Data collected about millions of cancer patients—their pathology slides, imaging, and other tests—contain answers to many open questions in oncology," she explains. Her goal is to unlock those answers, transforming how we predict, prevent, and treat cancer.

Her AI model isn't just a diagnostic tool; it's a prophecy engine, offering the most valuable currency in medicine: time. Time to act, time to prevent, time to save lives.

In transforming her personal battle with cancer into a technological revolution, Barzilay exemplifies how the convergence of AI and medicine can create tools that don't just treat disease, but anticipate it—turning the dream of preventive medicine into reality, one algorithm at a time.

Dr. Regina Barzilay's most recent work represents a monumental shift in her career, moving from the intricacies of natural language to the profound complexities of human biology and medicine. Her contributions are twofold: she has developed AI models that are revolutionizing both clinical diagnosis and molecular discovery. The Mirai and Sybil models are transforming preventative care by providing a new paradigm for cancer risk assessment, enabling earlier detection and more personalized screening strategies. Simultaneously, her work in molecular discovery, from the groundbreaking discovery of the antibiotic Halicin to the open-source democratization of drug design with the Boltz models, is fundamentally re-engineering the research and development pipeline for new therapeutics.

MIRAI has been validated in hospitals around the world. In the US, Mass General Brigham, Emory, and Novant and MIT’s Jameel Clinic have thoroughly tested the technology. And internationally it has been tested in research and pilot settings in (Karolinska (Sweden), Maccabi-Assuta (Israel), Chang Gung Memorial (Taiwan), and Barretos Cancer Hospital (Brazil)). Yet there is no evidence that I’ve found that leading U.S. centers like Memorial Sloan Kettering or MD Anderson have incorporated MIRAI into routine, all-patient screening protocols.

This highlights a systemic failure in how life-saving innovations reach patients. Even when technologies are proven in large, independent studies, foot dragging by hospital systems, insurance companies and politicians delays adoption for years. Patients are left waiting.

When I asked AI:

I wonder how much of this is driven by radiologist resistance -- they would be much less needed with the advent of this technology, which can do what they do much more reliably, and at a fraction of the cost. And even, though it is a very negative and upsetting thought, hospitals themselves. Cancer clinics would be much less needed if cancers could be identified years in advance.

AI responded (bolding as generated by ChatGPT 5.0):

You’ve touched on something very real — and very uncomfortable — in the world of medical innovation: entrenched interests and economic incentives. These forces can slow the adoption of tools that are, on paper, more accurate, cheaper, and more scalable. Let me unpack it in two layers: radiologists, and hospitals/clinics.

1. Radiologists and Professional Resistance

• Job security concerns: Radiologists often fear that AI will replace key parts of their role — especially in image interpretation, which is the bread and butter of their profession.

• Identity and expertise: Medicine isn’t just a job; it’s years of training, status, and authority. If a machine can read mammograms more accurately than the human eye, that threatens not just livelihoods but professional identity.

• Regulatory cover: Professional societies (like the American College of Radiology) often lobby regulators to take a cautious stance on AI. They argue it’s about patient safety, but it also aligns with protecting their role.

That said — many radiologists acknowledge AI’s power and are reframing their role: from “sole diagnostician” to “human overseer of AI.” This way they stay relevant, but adoption is slowed by the need to balance both.

2. Hospitals and Cancer Clinics

• Revenue structures: Hospitals (especially cancer centers) generate massive revenue from procedures, imaging, and treatments.

  • If a tool like MIRAI shifts cancer detection 5–10 years earlier, that reduces late-stage diagnoses — and late-stage treatment is where hospitals earn billions (chemotherapy, radiation, surgery, long-term monitoring).

  • Prevention and early detection are good for patients, but they often undermine the current revenue model.

• Investment inertia: Hospitals have invested heavily in existing infrastructure (equipment, personnel, billing systems). Moving toward predictive tools threatens to make some of those investments less valuable.

• Risk and liability: Adopting new technologies can expose hospitals to legal liability (“Why didn’t you use the gold standard?”). Ironically, sticking to the old standard protects them legally, even if it hurts patients medically.

3. The Deeper Structural Issue

• The “standard of care” is often defined by consensus, guidelines, and reimbursement rules — not just by the best available science.

• As long as reimbursement flows toward late-stage treatment, hospitals and insurers are financially disincentivized to pivot toward prevention, even if prevention is more humane and scientifically sound.

• In other words: the system is not designed to reward earlier, cheaper, better care.

Final thoughts:

I encounter the mindset, “Hospitals would be a great place to work if not for the patients,” very often in my work as an advocate for clients. And I did encounter it often in my own experiences as a cancer patient. When I was diagnosed with stage 4a non-Hodgkin’s lymphoma thirty-five years ago, the first oncologist I saw told me there was no good answer — no cure. If I had accepted that, I would not be alive to write these words. Instead, I went through twelve oncologists until I found a path forward, and two kind-hearted physicians. Persistence saved my life.

This is the reality for many patients. The standard of care may offer nothing, but science is always moving ahead. Most patients never learn what options exist beyond that first opinion. Many die unnecessarily. Or, more accurately, prematurely because we all die sooner or later.

I do everything I can to help patients find and access the latest, emerging therapies. Sometimes it is just a matter of being persistent. And sometimes, when necessary, it requires causing a runkus.

Note on the video.

The more I do this work, the more convinced I become in the necessity of having a survivor’s mindset — an unwillingness to give up — a resourceful mindset in combination with in-depth research into possible cures after one has received a terminal diagnosis.

In this video, I combined a video I created on that combination with a video NotebookLM created based on the article below. NotebookLM creates videos that are just okay, but over time I hope to develop other, better, techniques. In the meantime it is an adequate summary of this Substack.

. . .

In 1990, a couple of grape-sized lumps appeared on the side of my neck. They wouldn’t go away so I went to my local doctor. He told me that I had a reaction to insect bites and it was nothing to worry about. They would go away on their own. In those days I ran a lot in the woods and frequently got deerfly bites on my neck and shoulders.

Months went by. No change, so I phoned a radiation oncologist at a nearby hospital and made an appointment. He took a blood test, looked at my neck and said, “I think you’ve got cancer. But, then again, I think everyone has cancer, because that’s all I see all day long. So we need to do some tests – a bone marrow aspirate, a lymph node biopsy.” He phoned me a few days after the tests and told me that I had stage 4a non-Hodgkin’s lymphoma. Stage 4a means it had infiltrated my bone marrow. There was no known cure. I had two young sons, four and one years old. My world felt like it was closing in on me.

It took me over ten years to find a cure. First, there was a couple of years of “wait and see” – there is no statistical advantage to early treatment for indolent lymphomas. The disease attacks your bone marrow as does chemotherapy so sooner or later lymphoma patients did, in those days, go into a downward spiral. Chemo attacks fast-growing cells – that’s why people experience nausea (stomach lining destruction) and hair loss, among other side effects. Indolent lymphoma is relatively slow growing. The chemo first kills off the fastest-growing cancer cells. The slow-going lymphoma cells replicate and gradually wear down a patient’s bone marrow, a major engine in immune systems. Eventually, with depleted bone marrow, the cancer takes over. As I understand it, fungal infections are also often a problem.

I went through eleven oncologists, one naturopath and one osteopath over the next twelve years. Most were experts in the problem. Given that there was no proven cure, I needed a doctor with imagination, someone experimental by nature, but also someone with state-of the art expertise. I travelled a lot of dead ends including a clinical trial at the NIH in Bethesda, Maryland and one at the Dana Farber in Boston. I tried 714-x – a drug I had to inject daily into the area just above my groin. I bought it from someone operating out of a garage outside of Sherbrooke, Quebec. A friend gave me Andrew Weil’s home phone number. Dr. Weil has written many books on natural healing, including the bestseller Spontaneous Healing. He answered the phone and in a grouchy almost angry voice told me to eat maitake mushrooms and hung up. I did, and not only that began a study of all wild mushrooms. Eventually I met with Paul Stamets, widely regarded as one of the world's leading mycologists and a preeminent expert on North American fungi. I ate lots of wild mushrooms. My cancer kept growing.

I read books, including one on macrobiotics. Then I went to several health food stores in the area where I was living and asked if anyone knew of a macrobiotic chef in the area. Everyone mentioned the same guy who had not only studied with Michio Kushi, the primary advocate for macrobiotics in North America, but also had a phenomenal organic garden. I paid him for cooking lessons. When his marriage broke up, he and I rented a house together. Years later Michio, his wife and daughter all died of cancer. It didn’t work for me either. But I kept looking, kept experimenting. When it came time to get chemotherapy – I had 52 tumors throughout my body, including one in my mesentery that was 22 x 17 cm -- I convinced an oncologist in Canada, to give me Fludaribine, an experimental chemotherapy. I had to check into the hospital and get a 24 hour a day infusion. I had always been interested in painting, so while there I took books of art that resonated with me out of the hospital library, all the while tethered to an IV pole. I took them back to my room and painted the same images over and over until I learned the art of watercolor. The cancer kept growing.

I began a regimen of CHOP, the standard chemotherapy for non-Hodgkin’s lymphoma. The cancer kept growing. I enrolled in an experimental clinical trial at the Dana Farber for an autologous bone marrow transplant. In preparation, to reduce my tumor burden as much as possible, I got more chemo. My blood counts plummeted, I was anemic, a borderline hemophiliac and had shingles. My blood counts were too low to proceed, so was removed from the clinical trial. My oncologist told me that there was nothing more she could do for me. I was on my own.

Actually, in retrospect, it may have been a good thing that I was removed from that experimental bone marrow program. Everyone I read about who underwent the treatment died of complications even if cured of cancer. A prominent example was Paul Tsongas, the Democratic candidate for president in 1992. He went into remission from lymphoma but died of liver failure thirteen years later. The liver failure was linked directly to the treatment.

During this entire period, I was continuing to read about experimental therapies, phoning physicians, clinicians and medical researchers. I attended conferences and during breaks would track down the physicians who spoke and ask them, “What’s the most exciting research or clinical trial right now in indolent, B-cell, non-Hodgkin’s lymphoma?” I’d call authors of research papers. Most of them wouldn’t come to the phone, but those that did pointed me in interesting directions.

Somewhere during this time, I heard an interview on NPR that was important for me. A young man who had rowed alone across the Atlantic, and gotten blown off course, ran out of food and almost out of fresh water, said that he chanted to himself, over and over, “I am a survivor. I am a survivor. . .” After listening to the interview, that became my mantra too. Before I fell asleep at night, and as soon as I woke up in the morning, I chanted to myself, “I am a survivor.” I can’t prove it of course, but I think it was important to my recovery. It guided me in the right direction, even if on a subconscious level.

I came across an article about German physicians who returned from volunteering at Chernobyl exposed to sublethal radiation. They had skin lesions and internal bleeding. A chemist there, Friedrich-Wilhelm Kuehne, had developed a successful skin cream to treat severe burns. He modified it for these radiation skin lesions and it worked. He then created an infusion version for internal bleeding. Not only did it work, but none of the physicians got leukemia, a very common side effect of radiation exposure. That compound was WF-10.

Next, a urologist who had prostate cancer that had metastasized, and who happened to be a relative of Dr. Kuehne, decided to experiment on himself. He underwent a lethal dose of radiation to cure the prostate cancer, then took WF-10 to combat the effects of the radiation. Though the therapy was extremely painful, he survived. Cancer cured.

During a visit to Halifax, Nova Scotia to interview an artist, Gabriel Gély, for Heron Dance, my art journal, I had a chance encounter with a former client from the days I worked on Wall Street. My head was bald from chemo. He asked me what was going on. I told him about my cancer journey. He mentioned that he had an investment in a company in Germany that made a drug that may have some cancer treatment value, WF-10. With his introduction, I contacted the company, obtained the drug, and found, after calling many, many physicians one willing to administer it in the US. Within a month my blood counts had improved and my cancer had stopped growing. I wasn’t cured, but I was, for the first time in years, doing well. I felt healthy.

WF-10 kept me going long enough for the release of Rituxan, a monoclonal antibody used to treat B-cell lymphoma. Rituxan, the first monoclonal antibody, was made in part by inserting mouse antibody genes into cultivated mammalian cell lines (typically Chinese Hamster Ovary cells) that then mass-produce the antibody. Production now occurs in engineered cultured cells. I assume it works because of human’s long association with rodents, and the resulting defense systems we’ve built up over the millennium to protect ourselves. Our immune systems encounter the rodent cells and mass produce a defense that then defends against the invader. The antibody created also works against lymphoma. Regardless of whether or not I’m right about that, it worked. My lymph nodes disappeared. My blood counts recovered with the exception of platelets. Those, now normal, have taken all these years to fully recover.

The story of WF-10 is one of both triumph and tragedy. Triumph because it has proven effective against a number of debilitating diseases: HIV and diabetes, for instance. It seems to have some efficacy against cancer but its real value may be in restoring the depleted immune systems of those who have undergone chemotherapy or radiation. The tragedy is that the company that produces it ran out of money in the FDA approval process – during the clinical trials. Companies frequently spend over a billion dollars getting a new drug approved, and that doesn’t count the many that fail to show efficacy during the process. WF-10 is currently available only in Thailand.

Here’s a history of WF-10 clinical trials:

WF-10 (TCDO) Clinical Trial Timeline

1. Oncology (Pancreatic Cancer)

• Trial: Phase II, adjunctive WF‑10 + capecitabine for unresectable pancreatic cancer

• Registration: EU Clinical Trials Register (EudraCT ID: 2004‑001621‑14)

• Sponsor/Site: Dimethaid AG; European Pancreas Center, University of Heidelberg

• Status: Ended December 15, 2008; published results:

WF10 Stimulates NK Cell Cytotoxicity by Increasing LFA-1-Mediated Adhesion to Tumor Cells

Here’s a paragraph from the research study published after the trial ended:

WF10, or Immunokine, is based on the chlorite ion matrix tetrachlorodecaoxygen and is currently being evaluated in various clinical indications including the adjuvant therapy of inoperable pancreatic cancer and various inflammatory conditions. WF10 is known to have various immunological effects by stimulating innate immune functions, while inhibiting adaptive immune functions [10]. In contrast to control animals, sublethally irradiated rats treated with WF10 developed significantly less hematological or solid tumors than controls [11, 12]. This antitumor effect of WF10 could be explained by stimulating the innate natural cytotoxicity against tumors. We, therefore, investigate the effect of WF10 on NK cell cytotoxicity.

2. Wound Healing & Radiation Injury

• Radiation Dermatitis / Mucositis (France, 1991)

  • Randomized crossover trial (103 patients) showing lesion improvement with WF‑10

  • No formal NCT/EudraCT ID found

  • Cited in Drugs R D (2004) review (PubMed, PubMed)

• Radiation Cystitis (Thailand, post-1998)

  • WF‑10 approved for postradiation cystitis following a trial in 20 patients

  • Used under physician request protocols in Germany (PubMed)

• Chronic/Difficult Wounds (TCDO vs. PVP‑iodine, Germany, 1986)

  • Controlled study: 29 patients treated with TCDO (Oxoferin), compared vs beta‑iodine; showed superior wound healing and granulation tissue quality (PubMed)

3. Hematology / Immune Dysfunction (Infectious & Diabetic Ulcers)

• Diabetic Foot Ulcers (Double-blind RCT)

  • Journal of Foot & Ankle Surgery, 2011 (published Mar 2013).

  • 40 participants: standard therapy + WF‑10 vs placebo.

  • WF‑10 group showed significant improvement in wound severity, reduced infection/inflammation, and better granulation formation.

4. Other Mentions

• Drugs R D Review (2004)

  • Phase III trial in AIDS patients. This was a 1995 randomized trial of late stage HIV patients. It was a multi centric clinical trial involving San Francisco General, Miami and 20 more centers. The study was in the follow up period of 2 years but closing could not be acheived for financial reasons. Interim data showed that WF10-treated subjects out-survived those that received the placebo. More on that trial and its results here.

  • Cervical cancer (1989), radiation-related issues, etc.

  • Reports WF‑10 is approved in Thailand for postradiation inflammatory conditions and available under Canada’s Special Access Program (PubMed)

Summary Table

Summary:

• WF-10 saw some success in wound healing, radiation injuries, and ulcers, though these were small-scale (Phase II or lower).

• Clinical development stalled post-2008, and no large trials or publications emerged after that.

• Today, WF-10 exists primarily through topical derivatives like Oxoferin/Oxovasin, used for wound healing.

A primary benefit of WF-10 is its ability to boost macrophages, a critical blood element in the immune system. Macrophages are phagocytes: they “swallow” and digest bacteria, viruses, dead cells, and debris. This helps clear infections and clean up tissue damage. After digesting microbes, macrophages display antigens (pieces of what they ate) on their surface. This alerts and activates T cells, which are key to adaptive (long-term, targeted) immunity. T cells play a role in emerging cancer treatments as described in the chapter of this book on CAR-T and CAR-NK therapies. Macrophages also release cytokines (chemical signals) that call in and direct other immune cells.

If you are interested in WF-10, please be in touch. I might not be able to figure out a way to have it administered in the US, but I’ll contact OXO Translational Science GmbH in Germany and get back to you with options.

Macrophages

In addition to WF-10, a number of other compounds are also effective at boosting macrophages. These are easy to get in the US, either through an oncologist or, in many cases, over-the-counter:

1. Pathogen-Derived Molecules

These are often recognized by pattern recognition receptors (PRRs) on macrophages:

• Lipopolysaccharide (LPS) – from Gram-negative bacteria, a strong activator via TLR4 (used experimentally, not safe as therapy).

• CpG oligodeoxynucleotides – bacterial DNA motifs, recognized by TLR9, drive macrophage activation.

• Flagellin – bacterial protein recognized by TLR5.

2. Cytokines (Immune Signaling Proteins)

• Interferon-γ (IFN-γ) – classically activates macrophages into an M1 phenotype (tumoricidal, antimicrobial).

• Granulocyte-macrophage colony-stimulating factor (GM-CSF) – promotes macrophage proliferation and functional activation.

• Tumor necrosis factor (TNF) – enhances phagocytic and pro-inflammatory capacity.

3. Small Molecules & Drugs

• Imiquimod (Aldara® cream) – TLR7 agonist, used topically for skin cancers and warts; boosts macrophage and dendritic cell activity.

• Lentinan – mushroom-derived β-glucan analogue, enhances macrophage and NK function.

• Muramyl dipeptide (MDP) – bacterial cell wall component, tested as an immune stimulant.

• Checkpoint inhibitors (e.g., anti-PD-1, anti-CD47 antibodies) indirectly enhance macrophage phagocytosis of tumor cells by lifting inhibitory signals.

4. Natural Compounds & Nutraceuticals

• Vitamin D – enhances macrophage antimicrobial responses, especially against tuberculosis.

• Curcumin (from turmeric) – modulates macrophage polarization (often shifting toward M2, anti-inflammatory).

• Resveratrol, quercetin – polyphenols that influence macrophage cytokine output.

• Probiotics – some Lactobacillus strains increase macrophage phagocytosis via gut-immune signaling.

5. Clinical/Experimental Therapies

• BCG vaccine (for TB and bladder cancer) – a classic macrophage stimulator; induces “trained immunity” and is used intravesically to activate macrophages against bladder cancer.

• Monoclonal antibodies (rituximab, trastuzumab) – don’t directly activate macrophages, but engage them via Fcγ receptors to mediate antibody-dependent cellular phagocytosis (ADCP).

• Cytokine therapies (IL-2, IL-12, IFN-α) – tested in cancer immunotherapy to augment macrophage and NK functions.

Of these, the following are non-prescription:

• β-Glucans (yeast, oats, mushrooms)

  • Found in dietary supplements and functional foods.

  • Activate macrophages via Dectin-1 and CR3 receptors.

  • Available as capsules, powders, or extracts (e.g., baker’s yeast β-glucan, reishi or shiitake mushroom products).

  • There are a number of versions of Beta Glucan on the market. I’ve used several. Notes on a particularly interesting one, Wellmune WGP, follow.

• Vitamin D

  • Essential for proper macrophage antimicrobial activity.

  • OTC in tablets, capsules, drops.

  • Deficiency is linked to impaired innate immunity.

• Vitamin C

  • Antioxidant, enhances macrophage phagocytosis and ROS production.

  • Widely available as OTC supplements.

• Zinc

  • Important for macrophage differentiation and function.

  • OTC as lozenges, tablets, multivitamins.

• Probiotics (e.g., Lactobacillus, Bifidobacterium strains)

  • Some strains stimulate macrophage phagocytosis and cytokine production.

  • Available in capsules, yogurts, fermented foods.

• Medicinal mushroom extracts (e.g., Reishi/ Ganoderma lucidum, Shiitake/ Lentinula edodes, Maitake/ Grifola frondosa)

  • Contain β-glucans and polysaccharides with macrophage-activating effects.

  • OTC as powders, teas, tinctures.

• Curcumin (turmeric extract)

  • Modulates macrophage activity; effects can be pro- or anti-inflammatory depending on dose and context.

  • Available as OTC supplements.

• Resveratrol / Quercetin (plant polyphenols)

  • Influence macrophage cytokine secretion and oxidative burst.

  • OTC in capsules or dietary sources (berries, red grapes, onions).

Beta Glucan is widely used in Europe as an immune system booster. It isn’t prescribed by physicians in America because it can’t be patented and therefore won’t have drug company sponsorship or marketing. It has, nevertheless, been the subject of at least 200 research studies, as indicated in this NIH article:

β-Glucan Metabolic and Immunomodulatory Properties and Potential for Clinical Application

Conclusions

β-glucans are natural molecules that have significant therapeutic promise, particularly as metabolic and immune-modulatory agents [189]. Enthusiasm for their therapeutic potential is reflected in the high number of clinical trials of β-glucans that have been completed or are in progress.

Cancer Therapy

One of the most interesting applications of β-glucans is for cancer treatment, specifically as an adjuvant to enhance “conventional” cancer chemotherapeutics. β-glucans regulate complement-dependent cytotoxicity (CDC). β-glucans are recognized as PAMPs, triggering the response of immune effector cells. This will then elicit an anticancer immune response through the formation of a complex. When they enter the bloodstream, they are bound by endogenous plasma anti-β glucan antibodies (ABA). This binding activates complement and complement protein iC3b binds to the ABA, resulting in β-glucans—ABA—iC3b complex [146,147]. This complex binds to immune effector cells and activates specific aspects of innate immune function including CR3 phagocytosis. The activation and formation of this complex facilitate the direct killing of antibody-targeted tumour cells [146,147].

This mechanism was observed in both a pre-clinical model [148] and demonstrated with whole blood from healthy volunteers. The authors found that anti-cancer properties are dependent on the formation of the complex with naturally occurring ABAs [146]. In the preclinical model, mice were administered anti-tumour monoclonal antibodies (mABs) in combination with β-glucans. Results showed that the dual treatment produced significantly greater tumour regression in both mammary and hepatic tumours. The combinational treatment had a greater effect on each treatment individually. Interestingly, mice that were deficient in CR3 or serum CR3 or granulocytes did respond to treatment [148].

. . .

Years ago, in my research into Beta Glucan, I contacted a scientist at Biothera Pharmaceuticals, a manufacturer in the US of Beta Glucan, who told me not only that his company was doing research for the US Government into the use of Beta Glucan to treat military personnel, should they become exposed to radiation, but that he was giving it to his mother who had lymphoma. He thought that the drug was having a positive impact but had no specifics to share. Of particular importance, and something I’ve not seen or heard anywhere else, he told me that Beta Glucan was much more effective taken on an empty stomach. I get up a three times a night to use the bathroom, and for years, when it became impossible to get WF-10, I took 2100mg of Beta Glucan with each urination. I followed this practice during the night to take advantage of my empty stomach.

The indication of a benefit to those exposed to radiation was particularly interesting given that WF-10 has similar properties. This is what I found after further research:

Earlier preclinical research has demonstrated that yeast beta glucan enhanced the production of white blood cells – a process known as hematopoiesis – following radiation exposure and reduced infectious complications of myelosuppression. The findings support the potential for developing therapies for cancer patients undergoing radiation or chemotherapy, as well as protecting military personnel and civilians from "dirty bombs" or nuclear accidents.

Funding for the study came from the National Institute of Health, the U.S. Army Breast Cancer Research Program, the Kentucky Lung Cancer Research Board and Biothera

and this:

What the Evidence Shows

1. Early Research at the U.S. Armed Forces Radiobiology Research Institute (AFRRI)

• Patchen et al. conducted important studies in the early 1980s demonstrating that administration of β‑1,3-glucan significantly helped mice recover from radiation-induced bone marrow injury and improved survival rates after lethal radiation exposure. These studies were experimental and preclinical, using purified forms of β‑glucan.
  “Currently, we [U.S. Armed Services] are using glucan to enhance hemopoietic proliferation in conjunction with hemopoietic injury induced by radiation.” betaglucandata.com+2doveclinic.com+2pmc.ncbi.nlm.nih.gov+3betaglucan.org+3betaglucan.org+3

• Additional reports noted that oral β‑glucan protected about 70% of mice from lethal radiation when given after exposure, and stimulated white blood cell recovery via CR3-mediated mechanisms.

The Biothera Beta Glucan is more expensive than others I’ve bought, but the purification process used probably makes it worth it. It is available on Amazon as Wellmune.

Biothera’s Contributions to β-Glucan Research & Standards

1. Development of Wellmune® (WGP®) – a Proprietary β-Glucan Ingredient

• Biothera developed and markets Wellmune WGP®, a highly purified yeast-derived β‑1,3/1,6‑glucan, removing mannans, proteins, and lipids to enhance bioactivity and purity.

• They emphasize that not all β‑glucans are alike, and biological activity varies by molecular structure and yeast strain. PMC+11NutraIngredients-USA.com+11SupplySide SJ+11

2. GRAS Status for Food Use

• In December 2006, Biothera’s WGP 3-6 and BetaRight® 3-6 became the first yeast β‑glucans to achieve Generally Recognized As Safe (GRAS) status under FDA guidelines—supported by extensive safety and manufacturing data. New Hope

3. Establishing Analytical Standards (GEM Assay)

• Biothera chemists introduced the Glucan Enzymatic Method (GEM)—a more accurate and precise assay for measuring β‑glucan content in raw ingredients and finished products. Published in the Journal of Agricultural and Food Chemistry, this method avoids polysaccharide misestimation inherent in older assays. Annals of Oncology+6SupplySide SJ+6NutraIngredients-USA.com+6

More on Beta Glucan can be found here. (I receive no compensation, direct or indirect, from any company or product mentioned).

The human immune system is a sophisticated and highly effective defense network, constantly working to protect the body from threats such as bacteria, viruses, and abnormal cells. Within this network are specialized cells that act as a security force, each with a distinct role. T cells, a specialized type of white blood cell, are trained to identify and eliminate individual threats with precision and efficiency. Another crucial component of this defense is a group of cells known as natural killer (NK) cells. These cells are equipped with an innate ability to recognize and destroy any cell that looks suspicious or damaged without needing prior training.

Despite this powerful natural defense, cancer cells are masters of disguise. They can develop mechanisms to evade immune surveillance, essentially becoming "invisible" to the T cells and NK cells trying to hunt them down. This ability to become immune-evasive is a fundamental challenge in cancer treatment. Even when the immune system recognizes a tumor, it may not have the right tools or a sufficient number of cells to overcome the cancer's defenses. This is why many traditional cancer treatments have focused on directly attacking the tumor, but this can cause significant damage to healthy cells as well.

This ability to become immune-evasive is a fundamental challenge in cancer treatment. Even when the immune system recognizes a tumor, it may not have the right tools or a sufficient number of cells to overcome the cancer's defenses. Traditional cancer treatments, focused on directly attacking the tumor, often cause significant damage to healthy cells as well.

The profound innovation of cellular immunotherapy, and specifically of Chimeric Antigen Receptor (CAR) therapies, is the development of a new approach: making the cancer visible again and empowering the body's own defense system to do the fighting. Once infused, these specially engineered cells have the ability to multiply to hundreds of millions, circulate throughout the body, and continue to hunt down any remaining cancer cells. This marks a new era in medicine where the treatment is not a drug that is cleared from the body, but a living, breathing force that can continue to work for years. The goal is to create a perpetual surveillance system that can prevent the cancer from ever returning.

A decade ago the idea of using the immune system as a cornerstone of cancer treatment was considered an exciting but niche area of research. Today it is one of the most transformative pillars of modern oncology. CAR-T and CAR-NK therapies (the T and NK correspond to the T and NK cells mentioned in the first paragraph above) transcend traditional methods like chemotherapy and radiation to offer highly personalized, "living medicines". CAR therapies, constructed using AI in labs, guide custom-made cells to targets and destroy them.

Each CAR molecule is a complex, lab-made protein that spans the immune cell's membrane, with a part of it extending outside and another part residing inside the cell. The external part, composed of fragments of synthetic antibodies, is the "GPS." It is specifically designed to bind to a unique protein, called an antigen, that is found on the surface of cancer cells. This binding mechanism is highly precise, allowing the engineered cell to latch onto one specific target. The internal part of the CAR transmits a signal that activates the immune cell to multiply and launch a lethal attack on the cancer cell it has identified. The precision of CARs allows immune cells to bypass the complex natural recognition processes that cancer cells have learned to evade. Researchers have continually refined the design of these CARs, with the field now exploring advanced generations of these molecules that incorporate additional features, such as the ability to produce helpful immune system signaling proteins, to make the cells even more powerful and resilient. This ongoing evolution highlights a broader trend: the focus of research is not just on finding new targets, but on making the very tool of therapy—the CAR—smarter, more effective, and more capable of overcoming the biological complexities of cancer.

The first time Emily Walsh, a 29-year-old with relapsed acute lymphoblastic leukemia (ALL), heard the term “CAR-T,” she thought it sounded like science fiction. She had already endured chemotherapy, radiation, and a stem cell transplant. Each time, her cancer returned. Her doctors told her the standard playbook was exhausted.

The CAR-T process was unlike anything she’d experienced. Doctors collected her own T cells, shipped them to a specialized lab, and genetically modified them to carry a chimeric antigen receptor (CAR) designed to target her leukemia. Weeks later, billions of reprogrammed T cells were infused back into her body.

The side effects were intense—fevers, chills, weeks in the hospital—but the results were extraordinary. Within a month, Emily’s bone marrow showed no detectable leukemia. She returned to teaching part-time, something she and her family had stopped daring to imagine.

CAR-T therapies like these have already changed the landscape for certain blood cancers. Clinical trials show complete remission rates of 70–90% in some relapsed leukemias and lymphomas. For patients who once faced certain death, these numbers represent hope of survival.

CAR-T cell treatment consists of four distinct steps:

1. Collection: The process begins with a procedure called leukapheresis, where a patient's blood is drawn through a vein and filtered to collect T cells. The rest of the blood is then returned to the patient. This process can be performed in one or more outpatient procedures, but for patients with rapidly progressing diseases, waiting for the cells to be manufactured can be a significant concern.

2. Engineering: The collected T cells are then sent to a specialized laboratory, often located far from the hospital, where they are genetically modified. This crucial step involves introducing the genetic instructions for the Chimeric Antigen Receptor (CAR) into the T cells.

3. Expansion: After being engineered, the new CAR-T cells are grown and multiplied in the laboratory until there are hundreds of millions of them. This process, from collection to completion, typically takes about 3 to 5 weeks, or sometimes up to two months, a period during which the patient's cancer could potentially progress.

4. Infusion: Once the living medicine is ready, the expanded CAR-T cells are shipped back to the hospital. The patient first receives a preparatory chemotherapy treatment to make their body more receptive to the new cells. The CAR-T cells are then infused back into the patient's bloodstream, much like a blood transfusion. Inside the body, the engineered T cells continue to multiply and begin their search-and-destroy mission against cancer cells bearing the target antigen. This personalized, meticulous process is one of the key reasons for the high cost and logistical complexity of CAR-T therapy, but it has yielded unprecedented results.

As of 2025, the U.S. Food and Drug Administration (FDA) has approved seven CAR-T cell therapies, each designed to target specific types of hematologic (blood) malignancies. The first of these therapies, Kymriah™ (tisagenlecleucel), was approved based on clinical trials that showed it could eliminate leukemia in the majority of children with relapsed acute lymphoblastic leukemia (ALL). Long-term studies have since demonstrated that many of these children have survived for years without their cancer returning. Since then, additional therapies have been approved for adults with blood cancers. These include Yescarta™ (axicabtagene ciloleucel) and Breyanzi™ (lisocabtagene maraleucel) for various lymphomas, as well as Tecartus™ (brexucabtagene autoleucel) for mantle cell lymphoma and adult ALL.15 In clinical trials for advanced follicular lymphoma, Yescarta was found to eliminate the cancer in nearly 80% of patients, with many remaining disease-free three years later.

Two CAR-Ts for multiple myeloma (cilta-cel / Carvykti and ide-cel / Abecma) are now allowed much earlier in treatment, after only 1–2 prior lines instead of many. That means patients can access them sooner, with better odds of benefit. The CD19 CAR-T Breyanzi now also covers chronic lymphocytic leukemia (CLL) and follicular lymphoma.

In a significant advancement, the FDA approved a new therapy, Aucatzyl™ (obecabtagene autoleucel), in November 2024 for adults with relapsed or refractory B-cell precursor ALL. The approval was based on trial results where 42% of patients achieved a complete response within three months. These treatments have been described as transformative for patients with advanced cancers that were not controlled by other standard treatments. The durability of these responses, with more than 30% of participants in trials for large cell lymphoma alive without any evidence of cancer five years after treatment, provides compelling evidence that CAR-T can offer the potential for lasting remissions, or even "apparent cures," in a patient population with very few other options.

The limitations of current therapies have prompted researchers to develop new and more intelligent CARs. One of the most promising strategies is the development of multi-target CARs which may prevent the cancer from escaping immune surveillance by simply shedding one of its target antigens. In a trial to treat glioblastoma, a notoriously aggressive brain cancer, a dual-target CAR-T therapy, targeted not one but two proteins—epidermal growth factor receptor (EGFR) and interleukin-13 receptor alpha 2 (IL13Rα2). The trial combined a regimen of NK cell therapy with the immune-boosting agent, and achieved 100% disease control in a small cohort of five patients. Notably, three of the patients responded to the treatment, with two of them achieving a near-complete response, which is meaningful in such a tough disease.

Solid tumors—like breast, lung, and colon cancers—present a tougher problem.

• They have a protective "fortress": A barrier of other cells that makes it hard for CAR-T cells to get in.

• The environment is toxic: The area around a tumor is low in oxygen and nutrients, which exhausts and kills the CAR-T cells.

• The targets are tricky. The proteins on solid tumor cells often look very similar to those on healthy cells, leading to dangerous side effects.

Nevertheless, there is progress. Researchers started testing a "switchable" CAR-T that can be turned on and off to reduce side effects. A trial has been launched for metastatic breast cancer. A clinical trial for neuroendocrine tumors (NETs) has shown early signs of efficacy, including a patient with stable disease and a 12% tumor shrinkage. The trial has successfully advanced to a higher dose, signaling positive progress and no major safety concerns. Small-scale clinical trials are also underway for a variety of other solid tumors, including ovarian, colorectal, breast, and prostate cancers. The field is maturing, moving from a single-target, one-size-fits-all approach to a more sophisticated, multi-faceted strategy to overcome the biological complexities of solid tumors.

What’s next in CAR-T

• In-body CAR-T: Scientists at Stanford showed it's possible to make CAR-T cells directly inside the body using techniques similar to mRNA vaccines. This worked well in mice and could make the treatment easier and cheaper, avoiding the need to remove and modify cells in a lab. Early human tests just started.

• Smarter “logic-gated” CAR-Ts: These are designed like switches — only attacking cells with the right combination of signals, so they hit cancer but spare normal tissue.

• Off-the-shelf CAR-Ts: The idea is to make CAR-T products from healthy donors, freeze them, and use them like standard drugs. Still in early stages, but could make treatment faster and cheaper.

Overall, the focus is on expanding CAR-T to solid tumors (like brain or breast cancer), reducing side effects, and making it work for more people, with many trials showing quick improvements in tough cases.

In addition to trials in CAR-T therapies, CAR-NK research is showing major promise. NK cells possess several unique advantages that make them a promising foundation for the next generation of cellular immunotherapy. Unlike T cells, natural killer (NK) cells are part of the existing immune system. They are generalists, programmed by evolution to recognize stressed or abnormal cells without requiring prior exposure. They have an innate intelligence; they can recognize and destroy cancer cells without needing prior exposure or activation, a capability referred to as "natural cytotoxicity". This built-in killing ability makes them a powerful tool. Furthermore, arming NK cells with a CAR adds a layer of refined precision to their already potent anti-cancer activity.

Also, CAR-NK therapy has a significantly more favorable safety profile compared to CAR-T. CAR-NK posing a much lower risk of inducing severe side effects such as Cytokine Release Syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS).

Finally, CAR-NK cells offer a crucial solution to one of CAR-T's biggest logistical challenges. Because they lack a specific T cell receptor, NK cells do not cause Graft-versus-Host Disease (GvHD) when transferred between different individuals. This fundamental biological difference means that CAR-NK therapies can be manufactured using cells from a universal donor, making them a suitable candidate for "off-the-shelf" products. The superior safety profile of CAR-NK is not coincidental; it is a direct consequence of the unique biology of NK cells, which allows them to bypass the major safety hurdles inherent in CAR-T therapy.

There are now about 50 ongoing trials testing CAR-NK for various cancers. At MD Anderson Cancer Center, a pioneering trial infused patients with CAR-NK cells derived not from the patients themselves, but from umbilical cord blood donors. This was a key difference: CAR-T requires weeks of custom manufacturing, while CAR-NK could potentially be an off-the-shelf therapy, ready in days and manufactured in large batches. These mass-produced cells can then be stored and made readily available to treat multiple patients as needed. This scalable approach directly addresses the major problems associated with CAR-T's individualized manufacturing, which is both time-consuming and incredibly expensive, often costing hundreds of thousands of dollars for a single treatment. The long manufacturing time for CAR-T can be a critical drawback for patients with aggressive, fast-progressing cancers, for whom a delay of weeks could be detrimental.

At the Annual Meeting of the American Association for Cancer Research (AACR) held in April 2025, trial results for a CAR-NK therapy with "logic gates" (built-in rules to attack only cancer cells) were presented indicating complete remissions in some patients with relapsed blood cancers.

The ability to create a simpler, more affordable, and scalable product is not just a logistical advantage; it has the potential to democratize this revolutionary treatment, making it a viable option for a wider population and in a greater variety of healthcare settings.

The central challenge of current research is combining the proven persistence and efficacy of CAR-T with the increased safety and lower cost of CAR-NK. Research is also combining CAR-T and CAR-NK with engineered immune cells that are programmed with checkpoint inhibitors, and even designing cancer vaccines that prime the immune system before cell therapy. In addition, combining CAR-NK with tiny particles (nanoparticles) is moving into early human trials.

Here’s a side-by-side comparison:

At Dana-Farber, Dr. Catherine Wu’s team has developed personalized vaccines that train T cells to recognize unique tumor neoantigens. Early trials in melanoma showed patients mounting durable immune responses, some remaining cancer-free years later. AI plays a crucial role here—scanning each patient’s genome, identifying the most promising targets, and simulating which neoantigens the immune system is most likely to attack. What once took months can now be done in days.

Meanwhile, companies like Kite Pharma, Novartis, and Fate Therapeutics are running large-scale trials of CAR-NK and “armored CAR-T” cells—engineered not only to target cancer but to resist suppression by the tumor environment.

Why AI Matters Here

AI is woven into every stage of these therapies. Machine learning helps:

• Identify which tumor markers make the best targets.

• Model how engineered cells will behave in the complex tumor ecosystem.

• Predict which patients are most likely to respond—or experience severe side effects.

• Streamline manufacturing, reducing time from biopsy to infusion.

In some labs, AI models now simulate entire “digital twins” of a patient’s immune system, testing thousands of possible CAR designs in silico before moving forward with real cells.

A Patient’s View

For Emily, the former teacher in remission from ALL, the details of receptors and vectors mattered less than the outcome. “They took my cells out, re-trained them, and sent them back to fight for me,” she says. “It feels like I’ve been given my own army.”

Her story—and those of hundreds of others—underscores both the promise and the unfinished work. CAR-T and CAR-NK are not panaceas. Relapse still happens. Access remains limited. Costs are staggering. But the fact remains: patients once out of options are alive today because of these living medicines.

The New Frontier

Dr. Carl June, one of the pioneers of CAR-T therapy at the University of Pennsylvania, describes the moment bluntly: “We’re at the Wright Brothers stage of cell therapy. We’ve shown it can fly. Now we need to make it safe, reliable, and available to everyone.”

That journey is underway. With AI accelerating discovery and logistics, with immunology pushing boundaries, and with patients demanding access, CAR-T and CAR-NK therapies are carving out a new frontier.

Not every patient will be saved. But more than ever before, the immune system itself—rewired by AI and biotechnology—is becoming one of medicine’s most powerful weapons against cancer.

Some of the most innovative research is moving beyond the principles of immunotherapy entirely. A recent study published in Nature introduced a radically different approach: the AUN bacteria cancer treatment, which operates independently of the immune system. Preclinical trials for AUN therapy have shown tumor destruction with minimal side effects and no signs of Cytokine Release Syndrome (CRS), a significant advantage over many immunotherapies. While still in the very early stages of development with clinical trials expected to begin within six years, this discovery challenges 150 years of assumptions about how to fight cancer and offers a lifeline to a previously underserved patient population.

Trials in other bacteria-based cancer treatments are ongoing. Here are some of the most promising results as of September 1, 2025:

• Clostridium novyi-NT: Demonstrated real tumor destruction in humans (necrosis and partial shrinkage), though with safety/tolerability issues.

• CRS-207 (Listeria-mesothelin): Showed improved survival in pancreatic cancer and strong disease control in mesothelioma, making it the most clinically encouraging bacteria-based therapy to date.

• Salmonella TAPET-CD / VNP20009 & JNJ-64041757: Validated tumor-homing and safety, but clinical benefits were modest compared to the above.

Dr. Julian Cruz was stuck. A clinical oncologist at a major research hospital, he was facing a patient, Mark, with a cholangiocarcinoma—bile duct cancer—that had metastasized to the liver. Standard chemotherapy had failed. The tumor sequencing had revealed a rare and puzzling mutation, a variant of unknown significance (VUS) on the BRAF gene. It wasn't the classic V600E mutation that had a known drug. It was an outlier, a typo in a word no one had seen before.

Julian had spent nights scouring PubMed, reading obscure journals, and sending emails to colleagues. The collective wisdom of oncology had no answer for him. He felt the crushing weight of the information age: too many papers, too many trials, too many tiny discoveries happening too fast for any single human to synthesize.

In desperation, he reached out to an old medical school friend, now a computational biologist, who said, “You have to talk to Olivier Elemento at Weill Cornell. He’s building a crystal ball.”

A week later, Julian was in Elemento’s office, which looked less like a lab and more like the bridge of a starship. Massive monitors displayed swirling, interconnected networks of nodes and lines—a knowledge graph.

"Think of it as a map of all human medical knowledge," Elemento explained. "But unlike PubMed, which is a library of books, this is a brain. It reads every paper, every clinical trial report, every drug database, and it doesn't just store them. It understands the relationships. It connects Drug A to Gene B, which is like Protein C, which is inhibited by Mechanism D, which was studied in a mouse model for Cancer E. It sees the threads we can't."

Julian provided Mark’s de-identified data: the genomic report, pathology images, MRI scans, and failed treatment history. Elemento’s team fed it into their AI platform, called The Digital Twin.

The process was not magic; it was a monumental act of connection. The AI didn’t “think.” It cross-referenced. It took Mark’s rare BRAF variant and scoured its knowledge graph. No direct match existed. But it found something else: a paper from a Japanese team studying a similar variant in melanoma. Another thread connected it to a known drug resistance pathway. A third thread linked that pathway to a specific combination therapy: a MEK inhibitor used for melanoma and a novel EGFR inhibitor being tested for lung cancer.

In minutes, the AI generated a report. It didn’t say “do this.” It said:

"Hypothesis: The patient's VUS may confer sensitivity to a trametinib (MEK inhibitor) and cetuximab (EGFR inhibitor) combination. Rationale: 87% similarity to resistance pathway modeled in Study X. Preclinical evidence in cell lines with analogous variants from Study Y. Two case reports of response in cholangiocarcinoma with overlapping genomic signatures."

It was a lifeline, a data-driven Hail Mary.

Julian took the report to his tumor board. It was met with skepticism. Using a melanoma drug and a lung cancer drug for a bile duct cancer was unorthodox. The insurance would never approve it. But they had no other options. Armed with the AI's detailed rationale, Julian applied for compassionate use access from the pharmaceutical companies.

He got the drugs. Mark started the combination therapy. The first scan six weeks later showed stability. The scan after that showed a 30% reduction in the liver metastases. It wasn't a miracle cure, but it was time. Precious, quality time that the standard of care could not have offered.

Stories like Mark’s highlight why AI-driven personalization matters. The “one-size-fits-all” model of oncology is giving way to therapy tailored to both tumor genetics and patient biology.

At Memorial Sloan Kettering, digital twins are now being tested systematically across dozens of cancers. At MD Anderson, AI systems analyze tumor biopsies, blood markers, and genetic profiles to predict immunotherapy response with 85% accuracy. At Stanford, new foundation models like MUSK integrate imaging, pathology, and genomics to forecast survival better than staging systems alone.

What once took months of guesswork is now emerging in real-time. For patients like Mark, this can mean not just treatment, but hope.

Radiation oncologist Dr. Maria Rodriguez was running behind. Again. Her 3 PM appointment, a frightened 72-year-old man with prostate cancer named Arthur, was waiting for his consultation. But Maria was stuck in the planning room, her back aching from hunching over a screen.

She was "contouring." On Arthur's CT scan, she had to meticulously draw, slice by slice, in three dimensions:

The GTV (Gross Tumor Volume): the visible tumor.

The CTV (Clinical Target Volume): the GTV plus a margin for microscopic spread.

The PTV (Planning Target Volume): the CTV plus another margin for patient movement and setup error.

And most importantly, she had to draw the OARs (Organs at Risk)—the bladder, the rectum, the femoral heads—and ensure the radiation dose to them was as close to zero as possible.

It was a high-stakes art project. One misplaced line could mean delivering a toxic dose to healthy tissue, causing lifelong incontinence or bowel damage. It took hours per patient. This bottleneck meant Arthur would wait weeks between his consultation and his first life-saving treatment, his anxiety growing with each day.

The Solution – The Automated Architect

Across the country, Dr. Dan Low and his team at UCLA were solving Maria’s problem. They had trained an AI on a vast archive of past, high-quality radiation plans. The AI learned the "style" of expert radiation oncologists—how they expanded the GTV to CTV for a prostate cancer versus a lung cancer, how they shaped the radiation beams to avoid the spinal cord, how they sculpted the dose to spare the rectum.

They called it the "Auto-Contour" AI.

The Transformation – From Draftsman to Editor

Two years later, Dr. Rodriguez’s hospital adopted the system. For a new patient like Arthur, she now simply ordered a "CT Sim" and uploaded the images to the platform. Before she’d even finished her coffee, an notification popped up: "Contouring Complete for Patient Arthur, John."

She opened the plan. There it was. The prostate, the seminal vesicles, the bladder, the rectum—all perfectly outlined in different colors. It was… excellent. It wasn't just a rough draft; it was a finished product that would have taken her and a dosimetrist half a day to create.

Her role transformed instantly. She was no longer the draftsman, painstakingly drawing every line. She was the editor-in-chief. She zoomed in, checked the AI’s work, made a tiny adjustment to the rectal margin based on her own experience, and approved it.

The entire process took fifteen minutes.

She walked into the consultation room to see Arthur and his wife. She showed them the plan on the screen. "See this?" she said, pointing to the precise red outline around the prostate. "This is exactly where we’re targeting. And see how this blue area, your rectum, is completely avoided? We’ve already designed your treatment. We can start tomorrow."

The relief on their faces was immediate and profound. The waiting, the worst part, was over.

For Maria, the AI had given her back her most valuable resource: time. Not just efficiency, but the time to be a doctor. She could now spend those extra hours with her patients, explaining, comforting, and guiding them through their journey. The machine handled the repetitive precision; she provided the human care.

Note: Contouring
Contouring defines tumor and organ boundaries for radiation. AI speeds this up and reduces risk of damage to healthy tissues.

Dr. Eliezer Van Allen, an oncologist at Dana-Farber Cancer Institute, saw the clinical potential immediately. "If we could understand the genetic drivers of each patient's cancer, we could choose targeted therapies with precision," he realized.

Van Allen led the development of a comprehensive genomic profiling program that uses AI to analyze tumor DNA and recommend personalized treatments. The system examines not just well-known cancer genes, but the entire genome, looking for any actionable mutations.

The impact on patient care has been profound. Sarah Martinez, a 34-year-old teacher from Boston, was diagnosed with metastatic lung cancer despite never smoking. Traditional chemotherapy had limited effectiveness, but genomic analysis revealed her tumor had a rare genetic fusion.

"The AI analysis identified a targeted drug that matched her specific genetic alteration," Dr. Van Allen explains. "Within three months, her tumors had shrunk dramatically. She's now been cancer-free for two years."

Dr. Alice Shaw, a lung cancer specialist at Massachusetts General Hospital, has seen similar transformations. "We've moved from giving everyone with lung cancer the same chemotherapy to having dozens of targeted options based on genetic analysis. AI makes this personalized approach possible at scale."

At the University of Texas at San Antonio (UTSA), Dr. Aimin Chen and collaborators, including Dr. Nikos Papanikolaou at UT Health San Antonio, are using AI to personalize radiotherapy. Their generative-AI workflow fuses weekly cone-beam CT (CBCT) scans with planning CTs to track tumor shrinkage and adjust radiation doses.

In a study of 16 lung cancer patients over six weeks, the system reduced healthy lung exposure and cut the predicted risk of radiation-induced pneumonitis by up to 35%. “This is a wonderful example of how artificial intelligence can be used to develop new personalized treatments for the benefit of society,” Papanikolaou said.

Note: Adaptive Radiotherapy
Rather than using a static plan, adaptive radiotherapy updates treatment weekly to reflect current tumor size and shape. AI makes this practical for real-world clinics.

Predicting treatment response: AI can analyze individual patient data (genomics, clinical data, imaging) to predict how likely a patient is to respond to a particular treatment, such as immunotherapy. The LORIS tool, for instance, uses six simple variables from routine clinical data to predict resistance to PD-1 inhibitors in advanced melanoma patients.

Optimizing treatment plans: AI can help personalize treatment plans by identifying specific molecular targets for therapies and making real-time adjustments based on tumor response.

• Improving radiotherapy and chemotherapy: AI can precisely delineate tumor boundaries for radiation treatment, optimize dosages, and predict neutropenia risk in chemotherapy patients.

Real-time decision support: AI platforms may provide continuous, real-time recommendations based on patient data and the latest research.

• Adaptive clinical trials: AI-powered trials could dynamically match patients to therapies based on their tumor characteristics.

• Digital twins: Future AI systems may create "digital twins" of patients by integrating various data to simulate disease progression and personalize care.

• Proactive prevention: AI could integrate data from various sources to predict cancer risk and detect recurrence earlier.

At Dana-Farber, Dr. Catherine Wu had spent years unraveling the complexities of cancer genomics. Her lab was renowned for decoding the mutations that drive cancers and for pioneering personalized cancer vaccines. But as the flood of sequencing data grew, so did the challenge of making sense of it all.

The old approach—looking at a single biomarker or a handful of mutations—was giving way to a new paradigm. AI could ingest thousands of variables across millions of patients simultaneously, finding relationships invisible to traditional analysis.

Dr. Sridhar Ramaswamy, former chief medical officer at Flatiron Health (now at Veracyte), witnessed this transformation firsthand. "We went from asking 'What does this one biomarker tell us?' to 'What can we learn from analyzing thousands of variables across millions of patients simultaneously?'"

This shift has profound implications. Instead of one-size-fits-all treatments, oncologists are increasingly able to tailor therapies to individual patients based on the unique molecular signature of their tumors, their genetic predispositions, and even their lifestyle factors.

Wu’s group demonstrated this by designing personalized vaccines that trained the immune system to recognize each patient’s unique tumor “neoantigens.” In early trials, patients with melanoma who received these custom vaccines showed durable immune responses, with some remaining cancer-free years after treatment.

AI was critical in the process: it scanned each patient’s genome, identified the most promising neoantigens, and simulated how T-cells would recognize them. What once would have taken months of painstaking lab work could now be done in days.

The broader vision is a future where no two cancer patients are treated alike. Genomics, imaging, pathology, and clinical data—integrated by AI—will generate treatment blueprints as unique as fingerprints.

While these unique customized treatments for individual patients are only currently available at very few major cancer hospitals and research centers, they are the future.

Source: Documented findings and patient cases reported 2023–2025 by Weill Cornell Medicine (Elemento’s Digital Twin), UCLA (Auto-Contour AI), Dana-Farber and MGH (Van Allen and Shaw), UTSA & UT Health San Antonio (Chen and Papanikolaou), with publications in Nature Medicine, npj Precision Oncology, JAMA Oncology, and conference proceedings.

Other important recent breakthroughs in AI-driven cancer research.

1. Diagnosing Tumors with MRI
18 Aug 2025 – npj Precision Oncology
Dr. Ping Yin and colleagues developed an AI model that distinguishes between benign and malignant pelvic and sacral tumors using standard MRI without contrast dye. The system matched radiologists’ accuracy while avoiding the risks and costs of contrast agents.
Source: “End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study,” npj Precision Oncology, 18 Aug 2025.
Key Implication: Patients may soon undergo safer, faster, and more affordable MRI scans without needing contrast dyes.

2. Predicting Patient Drug Response
15 Aug 2025 – npj Precision Oncology
Researchers introduced PharmaFormer, which adapts knowledge from large drug response datasets using patient-derived tumor organoids (“mini tumors” grown from patient cells) to predict how real patients will respond to treatment.
Source: “PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid,” npj Precision Oncology, 15 Aug 2025.
Key Implication: Oncologists could use organoid-trained AI to select the most effective drugs before exposing patients to trial-and-error therapies.

3. Interactive 3D Tumor Mapping
13 Aug 2025 – npj Precision Oncology
An interactive 3D AI-assisted segmentation tool for oropharyngeal cancer allows clinicians to outline tumors more quickly and precisely, combining manual control with AI guidance.
Source: “Interactive 3D segmentation for primary gross tumor volume in oropharyngeal cancer,” npj Precision Oncology, 13 Aug 2025.
Key Implication: Faster, more accurate tumor mapping could reduce delays in radiation therapy and improve targeting of cancer cells.

4. Body Composition Analysis in Multiple Myeloma
5 Aug 2025 – Scientific Reports
Researchers used AI to analyze CT scans of patients with multiple myeloma, measuring body fat and muscle mass. These body composition features helped predict how the disease might progress, providing new prognostic markers.
Source: “AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma,” Scientific Reports, 5 Aug 2025.
Key Implication: Doctors may soon personalize treatment plans based on a patient’s muscle and fat composition, not just tumor markers.

5. Predicting Immunotherapy Response
21 Jul 2025 – Scientific Reports
A method called HAPIR (Hallmark gene set-based machine learning) predicts which cancer patients are most likely to benefit from immunotherapy, addressing the challenge that only ~20–25% respond today.
Source: “HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients,” Scientific Reports, 21 Jul 2025.
Key Implication: By identifying likely responders, HAPIR could spare patients from ineffective treatments and direct resources where they will help most.

6. Conversational AI for Genomics Data
17 May 2025 – Communications Medicine
Melvin, a voice interface built on Amazon Alexa, allows clinicians and researchers to query complex cancer genomics datasets simply by asking questions aloud.
Source: “Melvin is a conversational voice interface for cancer genomics data,” Communications Medicine, 17 May 2025.
Key Implication: Researchers and doctors may access lifesaving genomic data more quickly and intuitively, accelerating discovery and care.

7. AI Evolution in Colorectal Cancer
25 Jun 2025 – Annals of Surgical Oncology
Dr. Christopher Lieu, Co-Director of GI Medical Oncology at CU Anschutz, co-authored new consensus guidelines for managing colorectal cancer with peritoneal metastases. He emphasized AI’s role in biomarker detection, therapy tailoring, and trial matching: “Everything changed in weeks for metastatic patients… Now’s the time.”
Source: “Consensus Guideline for the Management of Colorectal Cancer with Peritoneal Metastases,” Annals of Surgical Oncology, 25 Jun 2025.
Key Implication: Patients with advanced colorectal cancer may gain earlier referrals, more effective systemic therapies, and better access to clinical trials.

8. Cancer Cells Reprogrammed to Normal Cells – The KAIST Team's BENEIN Breakthrough

At the Korea Advanced Institute of Science and Technology (KAIST), researchers unveiled a computer system named BENEIN (Boolean Network Inference), to map the genetic circuitry of colon cancer cells and essentially "reprogram" them to act like normal cells. Researchers were able to shrink mouse tumors 70% without chemo. They reprogramed colorectal cancer cells back into a normal-like state, rather than destroying them through traditional methods like chemotherapy or radiation.

This paradigm shift from destruction to healing uses AI to analyze genomic data, offering non-toxic alternatives. "We're turning cancer's machinery against itself," the team noted, paving ways for targeted therapies across cancers.

Key Implication: Instead of destroying cancer cells with toxic drugs, future therapies may reprogram them into harmless states — a potential paradigm shift.

Emerging Cures, founded and run by a two-time cancer survivor, finds these new therapies and get clients access to them.

Summary:

• Medical science is changing at a phenomenal rate. The new drugs, surgical procedures and diagnostic processes are vastly superior to those they are replacing. AI is a primary driver in this healthcare revolution.

• Many legacy healthcare providers are being left behind. The advancements are coming on so quickly that they are unable to keep up.

• If you have a life-threatening disease, you need to be aware of the new therapies, most of which are available in a very few hospitals and research centers.

• Emerging Cures finds and get clients access to these new therapies.

Healthcare Institutions Resist Change That Makes Their Services and Skills Obsolete

When dealing with a life-threatening disease, it is the leading edge of medical knowledge applicable to your condition that really matters. AI is the primary driver of exciting, effective new treatments. Research that took months as recently as a year ago now takes hours or even minutes to complete.

Most, but not all, healthcare institutions resist change. They are reluctant to admit that much of their knowledge is obsolete. If they don't know the latest science, they tend to be biased against it. The standard of care is increasingly out of date and irrelevant.

In a recent test on Healthbench, a new, rigorous benchmark developed by OpenAI specifically to evaluate large language models in healthcare scenarios, a simple AI platform designed by Emad Mostaque's company, Intelligent Internet Inc., II-Medical, scored 48%, while physicians, on average, scored 15%. (For more, watch the podcast Moonshots here.)

According to the medical databases Medscape and PubMed Central, total human medical knowledge is now doubling more than once a year:

• 1950: Doubled every 50 years.
• 1980: Doubled every 7 years.
• 2010: Doubled every 3.5 years.
• 2020: Doubled every 73 days.

Groundbreaking research is published daily in hundreds of journals. Advances are being made around the world, around the clock. Clinical trial results are published in a wide variety of databases. Even the most dedicated physicians can't keep up with the deluge of information.

AI Is The Primary Driver Of Change In Medicine

As an example of just one new tool that is having a profound effect: DeepMind's AlphaFold has created 3D images of essentially every known protein in biology (around 200 million), including all proteins in the human genome. Proteins are major factors in both cancer and Alzheimer's. In just a short while, this database has become a critical resource for medical researchers, helping them understand disease mechanisms and develop new drugs and vaccines.

Some other examples of the role AI is increasingly playing in medicine:

• AI systems are now capable of surpassing human diagnostic accuracy. FDA-cleared systems now reach a 98.7% sensitivity for stroke detection. AI software trained on large datasets of brain scans has proven "twice as accurate" as human professionals in interpreting CT scans, MRIs, and X-rays, and can identify exactly when a stroke occurred, which is necessary in determining medical and surgical treatments.
• Surgical interventions have seen AI-assisted robotic procedures reduce operative times by 25% and intra-operative complications by 30%, while improving surgical precision by 40%.
• AI-powered tools are significantly improving the detection of musculoskeletal injuries. They can spot more bone fractures than humans, a crucial capability given that urgent care doctors reportedly miss broken bones in up to 10% of cases. This AI assistance has the potential to avoid both unnecessary X-rays and missed fractures, streamlining care and improving patient safety.
• In pilot programs, AI triage systems reduce missed incidental findings in abdominal CTs by over 60%.
• An AI tool has successfully detected 64% of epilepsy brain lesions that radiologists had previously missed, identifying tiny or obscured lesions more quickly than the human eye could.
• AI can detect diseases years in advance with high confidence for over 1,000 conditions, often before symptoms manifest clinically. It can, for instance, identify the risk of acute kidney injury up to 48 hours before clinical signs appear, allowing for earlier intervention.
• In breast cancer screening, AI detects breast cancer more accurately, quickly, and consistently.
• In the realm of drug development, AI has accelerated timelines by an average of six months per drug, with some clinical trials experiencing up to 70% cost savings and 80% timeline reductions. AI integration, if implemented nationwide, would generate annual healthcare savings of between $200 billion and $360 billion in the United States alone.

AI-powered symptom checkers can now ask patients a series of questions and suggest appropriate next steps for care. By analyzing patient data, including genetic test results, lab reports, vital signs, and lifestyle factors, AI can identify risk factors and predict the onset of diseases (e.g., sepsis) before symptoms appear. The more widespread this system becomes, the more effective it becomes because data is constantly updated as results come back from the accuracy of earlier determinations.

A Primary Characteristic Of The New Therapies Is Drugs Custom-Designed To An Individual Patient’s Disease and Genetic Characteristics

Advanced therapies are now designed to target specific diseases and devise treatments based on precise molecular targets rather than the application of chemotherapy or radiation that might be effective in one person and fail in another, both with the same diagnosis. In achieving this, AI assesses the cancer’s genetic makeup, and that of the individual patient's, maximizing the effectiveness of the drug and minimizing adverse drug reactions. AI integrated with wearable devices can continuously monitor patient health, detect abnormalities, and even adjust dosage automatically (e.g., insulin delivery for diabetes).

One targeted therapy, monoclonal antibodies, represented 22% of FDA approvals in 2023. These are proteins made in a laboratory and designed to stimulate our immune system. They are used in treating cancer, autoimmune diseases, and infections like COVID-19. These "designer antibodies" are highly specific, binding to a single target, which allows for precise immune responses. In another huge advance in this area, AI was used to design new “binders” that can attach, for the first time, to shape-shifting amorphous proteins, including some implicated in cancer and Alzheimer’s.

Previously, it took drug companies months or years to design binders that attach to proteins. A new AI tool designed by a team from the University of Washington can design binders to grab many previously “undruggable” proteins. (AI-Designed Drugs Can Now Target Previously ‘Undruggable’ Proteins in Cancer and Alzheimer’s, A new AI tool opens the door for designer protein drugs that tackle pain, cancer, and brain diseases. Author: Shelly Fan, SingularityHub). This new AI software recognizes disordered proteins and generates binders to understand the target’s overall shape. It then recombines binding pockets in different configurations to make a library of binder templates. Finally, using a new AI technique called diffusion, it generates roughly a thousand pockets that “allow for trillions of combinations” that can grab onto wiggly proteins, study author Kejia Wu said in a press release.

There are many other examples of how AI processes vast amounts of medical research, genomic data, and scientific literature, uncovering patterns and insights that would be impossible for humans to find manually. AI prioritizes promising drug candidates and eliminates less viable ones early on. It can predict how different compounds will interact with biological targets and estimate crucial pharmacological properties like solubility, bioavailability, and toxicity. AI is being applied to uncover new applications of existing drugs to unrelated diseases, leading to new therapeutic uses, more efficient and successful trials, and bringing new therapies to patients faster.

Some Important Developments Come From Europe and Asia

Therapeutic areas such as oncology, hematology/immunotherapy, and neurological disorders consistently feature prominently among new FDA drug approvals. In 2024, the FDA approved 50 novel drugs, defined as those with active ingredients not previously authorized, alongside nine new cellular and gene therapy products, culminating in a total of 59 new medical therapies. In Europe, this number was 114 new medicines, including 46 novel compounds. A portion of the work of Emerging Cures is finding drugs whose efficacy has been proven in China, Europe, Australia and India and that are now undergoing trials in the US, or are otherwise available here.

Some Important Drugs Don’t Make It Through The FDA Due To The High Cost Of Meeting U.S. Requirements

The pharmaceutical industry spends an estimated $2 billion on average to bring a single drug to market. Companies and individuals behind some worthwhile drugs run out of money trying to get approval. I’m very familiar with one—WF-10. It saved my life. The drug saves lives every day in Thailand but is not available in the US because its sponsor, Oxo-Chemie, ran out of money trying to get through the FDA process. Searchlight Pharma, a company in Canada, now owns the North American rights but is doing nothing with them. (As far as I have been able to determine. I’ve reached out to them for confirmation).

We at Emerging Cures are constantly on the lookout, on behalf of our clients, for drugs that are championed by a respected physician or medical researcher, that are backed by strong data, but that didn’t make it all the way through the approval process for funding reasons. Some of these are available under compassionate release or other programs, either in Canada or the US.

What’s Next?

When I asked one of the AI platforms I use—I believe it was Gemini in this case—what impact AI will have on cancer medicine over the next six to twelve months, this is how it responded:

By mid-2026, AI's presence in cancer research will shift from being an innovative tool to an indispensable partner, driving increasingly precise, personalized, and rapid advancements in the fight against cancer:

1. Broader Adoption of FDA-Approved AI Tools: More AI-powered diagnostic and prognostic tools, particularly in medical imaging and digital pathology, will gain regulatory approvals and transition from research to widespread clinical use. The European Federation for Cancer Images (EUCAIM) project, aiming to establish a pan-European digital infrastructure for cancer images with at least 50 AI tools by 2026, exemplifies this push for integrated AI.

2. Advanced Multi-Omics and Multi-Modal AI: AI models will become even more sophisticated at integrating and interpreting truly vast and diverse datasets, combining genomics, proteomics, metabolomics, spatial transcriptomics, and high-resolution 3D imaging data. This will provide unprecedented insights into tumor microenvironments and immune responses, leading to the discovery of more effective targeted therapies and immunotherapies, including novel targets for antibody-drug conjugates (ADCs) and cancer vaccines.

3. Refined Liquid Biopsy and Early Detection: While multi-cancer early detection (MCED) tests are already advancing, the next 6-12 months will likely see further refinements in their sensitivity and specificity, potentially leading to additional regulatory breakthroughs and expanded trials for population-wide screening. AI will play a critical role in distinguishing true cancer signals from background noise and clonal hematopoiesis, improving the reliability of these tests.

4. Generative AI's Growing Influence in Drug Discovery: Generative AI models will move beyond simply predicting molecule properties to autonomously designing and optimizing entirely novel compounds with specific desired characteristics. This will further shorten drug discovery timelines and reduce R&D costs, with a higher probability of successful drug candidates entering preclinical and early clinical stages. The "turning point" for AI in precision oncology, as predicted for 2025, will be increasingly evident by mid-2026.

5. AI-Driven Adaptive Clinical Trials as the Norm: AI will be routinely used to design clinical trials that adapt in real-time to emerging safety and efficacy signals, allowing for faster evaluation of new cancer treatments. AI's role in patient stratification, predicting patient outcomes, and automating trial operations will become more central, making trials more efficient, cost-effective, and inclusive. Wearable technology integration for continuous patient monitoring in decentralized trials will become standard practice.

6. Focus on Explainability and Trust: As AI becomes more embedded, there will be increasing emphasis on developing more interpretable and "explainable AI" (XAI) models. This will allow researchers and clinicians to understand how AI makes its predictions, fostering greater trust, addressing ethical concerns around bias, and facilitating regulatory approval and widespread adoption.

7. Ethical Frameworks and Regulatory Evolution: Ongoing discussions and initiatives will lead to more robust ethical guidelines and clearer regulatory pathways for AI in cancer research. This will aim to ensure data privacy, address algorithmic bias, and define accountability in AI-driven decision-making, helping to guide the responsible deployment of these powerful tools.

Conclusion

AI is dramatically accelerating drug discovery, enhancing diagnostic accuracy, enabling personalized treatments, and boosting the efficiency of medical research. If you have a life-threatening disease, you need to fully understand what is happening in clinical trials and emerging therapies relevant to your condition. It might save your life.

Emerging Cures, founded and run by a two-time cancer survivor, finds these new therapies and get clients access to them.

To learn more about the work of Emerging Cures, visit www.emergingcures.org.

Summary

The future of cancer treatment is undergoing a huge transformation toward highly personalized, adaptive approaches. These are enhanced by continuous adjustment based on real-time data from liquid biopsies analyzed by artificial intelligence. This is nothing short of a revolution in treatment, though currently offered by only a few cancer treatment and research centers.

Methodology

AI was used in the creation of this report. First, it was used to summarize the hundreds of thousands of pages of NIH data on clinical trials, JAMA and other medical journal articles, university research papers and internet articles on the latest emerging medical science in cancer medicine. That generated 124 pages of summary and took about two hours. Several AI platforms were accessed– Gemini, ChatGPT, Perplexity, Claude, Grok, DeepSeek – but mostly on Grok (for its ability to scan Twitter/X) and Gemini Deep Research because it seems to be generating the most comprehensive results when it comes to medical science.

AI then was used to summarize all of that in non-technical language. That took about two minutes and generated 14 pages of results. Important details were then added from the 124 pages of more comprehensive reports.

From this, a two-page condensed version was prepared and a PDF of the longer 14 page summary report. The two page condensed version is below; the more detailed PDF can be accessed here.

Three treatment protocols in particular are dramatically improving the effectiveness of cancer treatment:

1. Precision Targeting

• Specific changes (mutations) in a patient's tumor cells are identified using detailed genetic tests. Then, therapies are chosen or designed to target the features unique to that specific cancer.

2. Immunological Innovations

• Cancer vaccines aim to "teach" or "boost" the immune system to recognize cancer cells as foreign and attack them, similar to how conventional vaccines protect against infectious diseases. There are preventive vaccines (like the HPV vaccine, which prevents cancers caused by viruses) and therapeutic vaccines, which treat existing cancers.

• CAR-T and CAR-NK Cell Therapies: These "living drugs" involve taking a patient's immune cells (T-cells or Natural Killer cells), genetically modifying them in the lab to recognize and attack cancer cells, and then putting them back into the patient. CAR-T cells precisely target specific antigens found on cancer cells, minimizing harm to healthy cells. They have shown remarkable success in blood cancers like leukemias and lymphomas.

3. Oncolytic Virus Therapy:

• Oncolytic viruses (OVs) are genetically engineered viruses designed to selectively infect, replicate within, and destroy cancer cells while leaving healthy cells unharmed. OVs kill cancer cells directly by making them burst. This bursting then releases bits of the tumor, which acts as an alarm for the body's immune system. This triggers a broader immune response against the cancer, even in areas not directly treated by the virus.

• Combination Potential: OVs are powerful when combined with immunotherapies. For example, a modified herpes virus (RP1) combined with an immune checkpoint inhibitor (nivolumab) has shown promising results in advanced melanoma, with tumors shrinking even in areas not directly injected with the virus.

Artificial Intelligence In Combination With Liquid Biopsies

• Applications range from aiding in patient diagnosis and prognosis to revolutionizing end-to-end drug discovery and development. The interpretation of vast genomic datasets, comprising billions of data points, is a task that far exceeds human capacity for analysis. AI excels in this area.

• AI analyzes medical images (X-rays, CT, MRI) and pathology slides to detect cancer with high accuracy, and can spot subtle signs missed by humans. Recent studies indicate that AI-generated diagnosis based on x-rays, CT scans and MRI is now 93%, significantly higher than that of either human radiologists, and even significantly outperforming radiologists combined with AI.

• Liquid Biopsies: These tests analyze cancer-related markers found in bodily fluids, most commonly blood. The advent of liquid biopsies represents a paradigm shift in cancer diagnostics.

• Liquid Biopsies in Combination With AI: A significant challenge, especially in early-stage cancers, is the minimal presence of tumor-derived biomarkers. AI, through its ability to analyze vast datasets, uses liquid biopsies detect cancer at earlier stages. Cancer treatments are continuously adjusted based on real-time data from liquid biopsies analyzed by artificial intelligence.

• Despite compelling evidence of their clinical utility, liquid biopsies are not yet routinely used, facing significant hurdles to integration into established clinical workflows. It is an important consideration, in choosing a cancer treatment center, whether or not they use liquid biopsies. Not only is it important in your treatment, but it is also indicative overall technological sophistication of the facility.

There are 13 major hospitals and perhaps another six research centers, mostly major universities, at the forefront of utilizing these new treatment regimens.

I do anything I can to facilitate a client’s participation in the most promising therapies.

By way of introduction, I conduct in-depth research into emerging therapies where the standard-of-care offers little hope of recovery and a quality life. My primary techniques are interviews of leading medical researchers and the use of AI to research trials and emerging cures. If the widely practiced standard-of-care offers the real promise of a quality life, and reasonable longevity, I’m probably of little use. Increasingly the standard-of-care offers real hope. In 2000, the five-year survival rate for cancer patients was around 63.5%. It is now approximately 70%. Similar trends exist in most other serious diseases.

In those cases where the standard-of-care offers little hope of a quality life, the first question someone on the receiving end of a dire diagnosis needs to ask themselves is, “Do I really want to live? Do I enjoy life?” The cancer journey can be hard. Partially, it is the disease itself. Partially it is the treatments. Chemo, surgery and radiation are hard on the body and on one’s mental attitude. Your friends and family can sympathize with you but not fully relate to or understand what you are going through. So it can be lonely. It is completely natural for someone recently diagnosed with a serious illness to contemplate an unfinished life and sink into self-pity over the dreams that will never be accomplished. The grandchildren (or children) you won’t hug and see grow up.

For the first few days after my first diagnosis, a terminal one, advanced non-Hodgkin’s lymphoma, I fell into a kind of daze. I saw life in a way I had not encountered before. I remember driving with my father to a bookstore to get some books on cancer, and looking out the car window thinking, “This is what life looks like when it is floating away. I wonder how many more times I’ll see this scene.” After a serious diagnosis, give yourself a few days to reflect on life, on its impermanence, and then decide how badly you want to live.

An athlete has to have deep drive to win. A business person needs a deep drive to succeed. In all of the big, important things in life, there is struggle. Surrender is fine. I’m not saying everyone has to go on the journey, has to want to sacrifice in order to survive. An oncologist once told me that he sometimes feels it necessary to say to patients, “If I want you to survive more than you do, we have a problem.” And, he said to me, he encounters a surprising number of people who, deep down, don’t really enjoy life that much and who don’t, as a result, really want to live. Or put effort into surviving.

If you decide to embark on the survival journey you need to know that setbacks will be encountered. Discouragement is a part of every journey that involves getting off the couch and going for it. Failure is always a possibility. Generally speaking, in fact, statistically, failure is the most likely outcome with the clients I work with. It certainly was my first diagnosis thirty-five years ago. Cancer patients either decide to put that possibility in their back pocket, or they decide to surrender. Doing the kind of work I do, putting my heart and soul into someone’s survival, I need a client who deep down wants to live.

Please allow me an analogy that many will find far-fetched, possibly even irrelevant. I have a background in wilderness survival. I see a commonality in the mindset that must be adopted by someone who encounters extreme adversity on a long wilderness journey, and someone on the cancer journey. It is the same mindset that is cultivated by athletes participating in extreme professional sports – boxing, professional football – and the mindset of special forces soldiers. That mindset is one of, “Come hell or high water, I am going to survive.”

Getting back to the wilderness example, imagine you are on a four hundred mile solo canoe trip down a remote, seldom traveled river. While the river is mostly flat, there are some rapids. Those rapids are rated as class II or III – generally within the skill level of an accomplished paddler such as yourself. However, at roughly the halfway point, you wrap your canoe around a big rock in the middle of the river and, as you swim for shore, notice your food bag disappear around a bend in the river.

On shore you collect your thoughts. Getting angry won’t help. Feeling sorry for yourself won’t help. You need to think one thing: survival. You need to cultivate and enhance, in any way possible, your relationship with your inner world, with your inner strength. That relationship will be profoundly shaped by the decisions you make over the next few hours.

As you start to look around, you notice something bob up and down in an eddy a hundred feet downriver – your lunch bag. You wade out and grab it. You return to shore and analyze your situation. In your lunch backpack is maybe three days of food. You are about two hundred miles upriver from your takeout – a place where the river intersects the road. Two hundred miles of moderately rough terrain is about twenty days of walking. In other words, seventeen days with no food. You can eat one day in six. Or, better, a sixth of a full ration each day.

As the crow flies though, you are a hundred miles from a road – a hundred miles of lakes, muskeg, mosquitos and black flies. You contemplate that option -- walking to the nearest road. You’ve lost your GPS and your compass. As an experienced wilderness traveler, you know that moss grows thickest on the north side of trees, and the prevailing winds in this area are from the north, so the treetops have a slight bias, a slight lean, toward the south. While setting off hoping to find the road, a hundred miles to the north in a straight line, may work, it also is risky. As anyone knows who has lost their way in the woods – and I have hundreds of times (I’m a daydreamer) – every time you step in one direction or another, for instance to get around a tree or boulder, you get slightly off course. Cumulatively, those minor changes add up to major course misdirection. You tend to end up going in a big circle. The lakes you need to walk around will not only add many miles to your hike out, but will probably throw you off course.

Equally important, if you stick to the river, you may come across your food pack. Or your tent and sleeping bag pack. Without those, bugs will consume a chunk of your physical and mental corpus. Sleep will be difficult. Your mental attitude will at times suffer because you will be suffering. On the other hand, there is a slight chance, walking along the river, that you may find both your food bag and your tent and sleeping bag pack. That would be extreme good fortune, somewhat akin to winning the lottery. Extremely unlikely, but possible. So you decide to stick to the river. The challenge there is that the trees, scrub and rocks may make sticking close to the river difficult, so you’ll need to constantly check, when you can’t see or hear the river, that you haven’t wandered away from it. If you do, death from starvation will likely result.

You think of something an old Inuit once told you – the translation of their word for hunting is “a long walk on an empty stomach.” That’s what you’ve embarked upon – a hunt for survival. I also remember something arctic explorer Helen Thayer once told me. On her solo walk to the North Pole, in the middle of the night, an extreme arctic blizzard blew away most of her food. She had a couple of bags of nuts left, nothing else. No radio to call for extraction. She was scared, knew the odds of her survival were greatly diminished. But on another level she told me, she was excited, almost euphoric. A little voice inside whispered, “When the chips are down, that’s when you shine. That’s where you excel.” Now is the time to nurture that little voice. That’s the voice that is going to get you through this.

So you collect your thoughts, grab your lunch pack, and set off. If you start to encounter extreme fatigue, or get overly discouraged, you stop, rest and maybe soak in the river a little to assuage your bug bites. Then you get up and walk.

Above all else, your mental attitude must be protected and preserved. Eyes on the prize: survival.

. . .

To read this post on the Emerging Cures website, visit here.