OpenEvidence is transforming how doctors access medical knowledge at the point of care, from the biggest medical establishments to small practices serving rural communities. Founder Daniel Nadler explains his team’s insight that training smaller, specialized AI models on peer-reviewed literature outperforms large general models for medical applications. He discusses how making the platform freely available to all physicians led to widespread organic adoption and strategic partnerships with publishers like the New England Journal of Medicine. In an industry where organizations move glacially, 10-20% of all U.S. doctors began using OpenEvidence overnight to find information buried deep in the long tail of new medical studies, to validate edge cases and improve diagnoses. Nadler emphasizes the importance of accuracy and transparency in AI healthcare applications.

Hosted by: Pat Grady, Sequoia Capital 

Mentioned in this episode: 

• Do We Still Need Clinical Language Models?: Paper from OpenEvidence founders showing that small, specialized models outperformed large models for healthcare diagnostics
• Chinchilla paper: Seminal 2022 paper about scaling laws in large language models
• Understand: Ted Chiang sci-fi novella published in 1991