All Work Published on Healthcare
This brief warns that AI systems that infer patients’ race in medical settings could deepen existing healthcare disparities.
This brief warns that AI systems that infer patients’ race in medical settings could deepen existing healthcare disparities.
Stanford researchers have developed a deep learning model that transforms overwhelming brain data into clear trajectories, opening new possibilities for understanding thought, emotion, and neurological disease.
Stanford researchers have developed a deep learning model that transforms overwhelming brain data into clear trajectories, opening new possibilities for understanding thought, emotion, and neurological disease.
Improving AI Software for Healthcare Diagnostics
This brief explores current regulatory frameworks for AI use in radiology and calls for stronger regulatory guidance to improve testing, enhance safety, and establish performance standards.
This brief explores current regulatory frameworks for AI use in radiology and calls for stronger regulatory guidance to improve testing, enhance safety, and establish performance standards.
Why 'Zero-Shot' Clinical Predictions Are Risky
These models generate plausible timelines from historical patterns; without calibration and auditing, their “probabilities” may not reflect reality.
These models generate plausible timelines from historical patterns; without calibration and auditing, their “probabilities” may not reflect reality.
This brief introduces AI-enabled depression prediction through social media and calls for clear policy guidelines to ensure patient privacy.
This brief introduces AI-enabled depression prediction through social media and calls for clear policy guidelines to ensure patient privacy.
Shana Lynch, HAI Head of Content and Associate Director of Communications, pointed out the "'era of AI evangelism is giving way to an era of AI evaluation,'" in her AI predictions piece, where she interviewed several Stanford AI experts on their insights for AI impacts in 2026.
Shana Lynch, HAI Head of Content and Associate Director of Communications, pointed out the "'era of AI evangelism is giving way to an era of AI evaluation,'" in her AI predictions piece, where she interviewed several Stanford AI experts on their insights for AI impacts in 2026.