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AI in Oncology — KOL Digest

KOL DigestMonday, August 31, 2026 All editions →

AI spots pancreatic cancer three years early, most oncologists use AI without governance — and Epic points AI at its own code

In this edition: Eric Topol, MD on AI detecting pancreatic cancer up to 3 years before diagnosis · Yan Leyfman, MD on the governance gap — 83% of surveyed oncology clinicians use AI independently, 71% report no institutional policy · Katy Beckermann, MD on Epic running Anthropic's Mythos across its own codebase · Chadi Nabhan, MD on AI tumor twins with Bishoy Faltas, MD.

Early detectionAI governanceHealth-system AIDigital twins
Eric Topol — profile photo, @EricTopol on X
Eric Topol@EricTopol on X
Physician-scientist; founder, Scripps Research Translational Institute.
The revolution in pancreatic cancer is not just about the new, impressive FDA approved drug or the vaccine treatment. It's also about AI detection up to 3 years before it is currently diagnosed. wsj.com/opinion/fda-ap… ↗
Newspaper opinion page titled 'A Pancreatic Cancer Revolution' on the FDA approval of daraxonrasib, with a highlighted passage reporting the Mayo Clinic's AI model could detect pancreatic cancer on routine abdominal CT scans up to three years before typical diagnosis.
View post on X ↗
Yan Leyfman, MD — profile photo, @YLeyfman on X
Yan Leyfman, MD@YLeyfman on X
Physician, Memorial Sloan Kettering; director, MedNews Week.
AI didn’t wait for oncology to build the rules. It simply arrived.

Today, clinicians are increasingly using AI to interpret complex information, navigate treatment options and clinical trials, and support decisions that are becoming more personalized—and more complicated.

But that raises an important question: Who is responsible when AI gets it wrong?

That question became the foundation for our new article on AI governance in precision oncology.

In our survey of 52 U.S. oncology clinicians, 83% reported independently using AI on personal platforms, while 71% reported no institutional AI governance policy and 77% were operating without institutional oversight.

Perhaps most striking was what we call the “verification paradox.” Nearly everyone intended to verify AI-generated recommendations—yet 23% would still have followed an intentionally embedded dosing error.

That matters even more as we move into increasingly complex areas of oncology: CAR-T therapy, bispecific antibodies, molecular classification, MRD monitoring, and highly individualized treatment decisions. These are areas where AI has enormous potential, but where confidently incorrect recommendations can also carry serious consequences.

Our conclusion is simple:

The oncologist of the AI era cannot simply be an AI user. We must become AI curators.

That means selecting the right tools, understanding their limitations, validating their performance, recognizing when they fail, and ultimately maintaining human accountability for the patient in front of us.

Technology will continue to evolve. Clinical accountability cannot.

I’m incredibly grateful to Dr. Arturo LoAIza-Bonilla MD for his tremendous mentorship and for continually challenging me to think more deeply about how we responsibly bring emerging technologies into cancer care.

I also sincerely thank the AIPO Journal, Dr. Douglas Flora, Dr. Nikhil Thaker, and Dr. Sanjay Juneja for providing a platform for these important conversations at the intersection of AI, innovation, and oncology.

The future of oncology will undoubtedly involve AI. The question is how we ensure that we remain worthy stewards of it.

journals.sagepub.com/doi/10.1177/29… ↗

#ArtificialIntelligence #Oncology #PrecisionOncology #AIinHealthcare #ClinicalAI #PatientSafety #CancerCare #PrecisionMedicine
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Katy Beckermann — profile photo, @katy_beckermann on X
Katy Beckermann@katy_beckermann on X
GU medical oncologist — clinical trials and drug development.
Epic EMR shared that AI using Mythos found security flaws their engineers had missed.

🔬 Epic ran Anthropic's Mythos models against its own codebase, several hundred million lines, under Project Glasswing
⚠️ The CIO conveyed that patching within 30 days is no longer good enough

How is your research IT handling validation when the LLM find issues in the code quickly? 🩺

#ClinicalAI #AIinMedicine #GlassWinga @claudeai
Summary card on Epic's Project Glasswing disclosure: Anthropic's Mythos models scanned Epic's several-hundred-million-line codebase and surfaced flaws engineers had missed; Epic's chief security officer quoted saying 30 days is no longer the patch target; noted as a vendor self-report without independent verification.
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chadi nabhan MD, MBA, FACP — profile photo, @chadinabhan on X
chadi nabhan MD, MBA, FACP@chadinabhan on X
Hematologist-oncologist; host, Healthcare Unfiltered.
AI Tumor Twins with Dr. Bishoy Faltas youtu.be/wpw4wBXveEM?is… ↗ via @YouTube

Streaming on @YouTube with @FaltasLab - as this research matures, it will be a game changer.

Check it out and subscribe.
View post on X ↗
Compiled and reviewed by the KOL Pulse research team, led by Brian Shields, Founder, KOL Pulse. Quotes are verbatim from their authors’ public posts on X. Last updated August 31, 2026.