Health Horizons

What if medicine stopped treating you like the average patient, and started treating you like you? MIT's Prof. Regina Barzilay has spent a decade building the AI algorithms to make that real, with a vision of cancer care where disease is caught far earlier, treatment involves less guesswork, and therapies have less side effects. Her current research takes on some of the hardest cases: metastatic patients in lung, colon, and breast cancer who've run out of options, using AI to predict which therapy is most likely to work for each individual. We get into why today's approach so often gives whole groups of patients the same treatment when only some will benefit, and why getting these tools to actual patients is its own hard problem. She also opens up about her own cancer diagnosis: how it reshaped which problems she thought were worth solving, and how she experienced the limits of treatments built for the average patient.00:00 Intro01:12 The future of personalized healthcare08:46 Current landscape of personalized treatments12:36 Regina's research: AI for metastatic cancer patients 16:30 Challenges in data and methodology26:42 Interpretability of the models27:40 Validation of the models31:33 Understanding foundation models in biology34:02 Challenges with biology foundation models38:53 The value of personalized treatments for all patients44:29 Regulatory challenges48:14 Regina's personal journey through breast cancer52:30 Overcoming Skepticism in AI Research55:38 The value of building a hospital network58:01 Lightning round01:01:01 Outro

Diabetic retinopathy is the leading cause of preventable blindness in the U.S — yet far too few people who need screening actually receive it. Through his company, Digital Diagnostics, and its flagship product LumineticsCore, Michael Abramoff and his team have built an FDA-authorized autonomous AI system that brings diabetic retinopathy screening much closer to patients.In this episode, we discuss how Michael has spent over a decade driving the systemic changes needed to make autonomous AI viable clinically, economically, and ethically.

From a farm in rural Kentucky to the cutting edge of molecular biology, Phil Sharp’s path is anything but ordinary. In 1977, he discovered RNA splicing at MIT—a breakthrough that redefined biology and won him a Nobel Prize. But his story goes beyond science—it’s also the story of how the biotech industry was born.In this episode, we explore:How did he go from a rural farm in Kentucky to a Nobel prize-winning discovery at MIT?How did the city of Cambridge become the most concentrated biotech ecosystem in the world?How to build trust in technology today, just as biotech had to build trust around recombinant DNA in the 1970s?

From radiology to the forefront of AI health regulation , Dr. Hugh Harvey has unique insights into the intersection between health, AI, and regulation - as well as some hot takes.Among other topics, we explored:Why is there not more adoption of AI despite all the hype around it?What are some misconceptions that companies have about the regulatory process?Should we always have a human a loop?