Mark Yatskar’s Talk: Scholarly Supervision for Medical AI: Radiology and Drug Design скачать в хорошем качестве

Mark Yatskar’s Talk: Scholarly Supervision for Medical AI: Radiology and Drug Design

Medical AI still leans heavily on painstakingly curated datasets that are expensive to build—and quickly become incomplete or outdated. But medicine is already self-documenting: methods, experiments, and results are written down every day in the scientific literature. In this talk, we show how to turn text from PubMed and PubChem into scalable supervision for two settings: clinical radiology and therapeutic discovery. For radiology, literature-built models achieve strong performance and transfer far more robustly across hospitals. For drug design, we introduce MedexCLIP, a multimodal foundation model of molecules and text trained from literature, enabling zero-shot prediction of safety and pharmacokinetic properties—and practical constraints for automated discovery pipelines. Together, these results position academic literature as a powerful, continually updated training signal for medical AI.