From Math to Medicine: Can AI Solve Healthcare’s Hardest Problems?

Free Clinical AI Briefs: https://www.healthit.com/signup?utm_s... · Course: https://oncologyaiacademy.com/course?... When a cancer treatment stops working, what should we investigate next? Could the computing power behind recent AI research help answer that question? Jivesh Sharma, M.D., and Talia compare AI-assisted mathematics, AlphaFold, AlphaGenome and Anthropic’s early ART enzyme-system findings. They ask what each advance establishes, where experiments are still needed, and how we would measure value for patients. The episode distinguishes proposed mathematical results from accepted proofs, DNA prediction from gene editing, and a research announcement from evidence of clinical benefit. It also examines treatment resistance, flow cytometry and the laboratory bottleneck. Better outcomes, preserved quality of life and more healthy years are the goals; these examples do not establish those benefits. Research cutoff: October 13, 2026. The outlook for the next few years is a forecast, not a validated patient-care workflow. Thank you for watching and listening. Follow HealthIT.com, like this episode and subscribe on YouTube. Which hard healthcare problem should come next? https://www.healthit.com/feedback Jivesh’s narration uses his authorized AI voice. Talia is an AI cohost. This is educational discussion, not individual medical advice. Chapters 00:00 The question 00:35 Navier–Stokes and mathematical scrutiny 01:40 Manuscripts, withdrawals and proof checks 02:30 Checking the intended claim 02:57 What more computing power buys 03:46 AlphaFold and the Nobel recognition 05:02 AlphaGenome: prediction, not editing 06:08 ART: discovery still needs experiments 07:11 Treatment resistance and flow cytometry 08:12 The laboratory bottleneck 09:12 Value that matters to patients 10:10 From research to patient benefit 11:21 Thank you, follow and share your question Sources OpenAI: Navier–Stokes announcement, September 8: https://openai.com/index/navier-stoke... Clay: September 11 statement: https://www.claymath.org/year_type/2026/ Clay: current problem status and official formulation: https://www.claymath.org/millennium/n... Buckmaster: concurrent mathematical work: https://cims.nyu.edu/~tristanb/statem... OpenAI: mathematics catalogue: https://github.com/openai/math Catalogue corrections, October 7: https://github.com/openai/math/blob/m... Proof/formalization critique, October 6 preprint: https://arxiv.org/abs/2610.08144 AlphaFold, Nature 2021: https://www.nature.com/articles/s4158... 2024 Nobel Prize in Chemistry: https://www.nobelprize.org/prizes/che... AlphaGenome, Nature 2026: https://www.nature.com/articles/s4158... DeepMind: AlphaGenome scope and efficiency: https://deepmind.google/blog/alphagen... Anthropic: early ART findings, September 23: https://www.anthropic.com/news/claude... ARUP project announcement, October 6: https://www.utahbusiness.com/press-re...