In this episode of the ODSC Aix Podcast, host Sheamus McGovern speaks with Dr. Regina Barzilay, a Distinguished Professor of AI and Health at MIT, and one of the leading voices in applying artificial intelligence to real-world medical challenges. Dr. Barzilay is also the AI faculty lead at MIT’s Jameel Clinic, where her work spans early disease detection, personalized treatment, and AI-powered drug discovery.

A recipient of the MacArthur “Genius” Fellowship, the AAAI Squirrel AI Award, and a member of both the National Academy of Engineering and the National Academy of Medicine, Dr. Barzilay has built a career bridging state-of-the-art AI with the pressing needs of patients and clinicians.

Together, they explore why AI tools that can accurately predict cancer and other diseases are still rarely used in clinical practice—and what it will take to bridge the gap between research and real-world care.

Key Topics Covered

• Why most diseases are diagnosed too late—and how AI can detect them before symptoms appear
• The story behind MIRAI (for breast cancer) and SYBIL (for lung cancer) predictive models
• Why powerful clinical AI tools aren’t widely adopted in hospitals and how the system can change
• What it takes to get machine learning models into real-world care across countries and hospitals
• The promise of generative AI in drug discovery and the discovery of new antibiotics
• Challenges in detecting and treating neurodegenerative diseases like ALS
• How AI could enable truly personalized medicine, predicting treatment side effects and responses
• The role of AI copilots in clinical workflows and the future of clinician-AI collaboration
• Why AI in healthcare must go beyond hype to deliver real, measurable value for patients

Memorable Outtakes

“The technology to save lives is here. We’re just not using it.” — Dr. Regina Barzilay on the gap between research and clinical adoption

“You can’t be an oncologist without using MRI or blood tests—but you can still practice medicine today without AI.” — Dr. Barzilay on structural resistance in healthcare systems

“Imagine giving some of your health data and getting back a real answer: what will happen to you. Not the population. You.” — Dr. Barzilay on personalized risk prediction and treatment modeling

References & Resources

Dr. Regina Barzilay  Academic Profile:https://www.csail.mit.edu/person/regina-barzilay LinkedIn:⁠https://www.linkedin.com/in/reginabarzilay/⁠

Resources Mentioned in the Episode

• MIRAI (AI model for breast cancer risk prediction): ⁠https://jclinic.mit.edu/mirai⁠
• SYBIL (AI model for lung cancer risk prediction):  ⁠https://news.mit.edu/2023/ai-model-can-detect-future-lung-cancer-0120⁠
• Halicin Antibiotic Discovery: 
• MIT Jameel Clinic for Machine Learning in Health:⁠ https://jclinic.mit.edu/⁠
• US Preventive Services Task Force (USPSTF):⁠ https://www.uspreventiveservicestaskforce.org/⁠
• Professor Dina Katabi’s AI monitoring research (referenced): ⁠https://www.csail.mit.edu/person/dina-katabi ⁠

Sponsored by

Agentic AI Summit 2025 Join the premier virtual event for AI builders from July 15–30. Gain hands-on skills in designing, deploying, and scaling autonomous AI agents. 🔥 Use code podcast for 10% off any ticket. Register now:⁠https://www.summit.ai/⁠

ODSC West 2025 – The Leading AI Training Conference Attend in San Francisco from October 28–30 for expert-led sessions on generative AI, LLMOps, and AI-driven automation. 🔥 Use code podcast for 10% off any ticket. Learn more:⁠ https://odsc.com/california