News & Announcements
Featured News
from the Division
Stanford Professor: How LLMs Influence Medical Diagnosis | Prof. Jonathan Chen
Dr. Jonathan Chen discusses startling research showing that AI can sometimes outperform doctors even when they are equipped with AI tools, challenging traditional “human-in-the-loop” assumptions. He explores the transition from AI as a tool to an active teammate, while warning against risks like anchoring bias and the 10–20% rate of harmful recommendations in current models. From his “ChatEHR” project to the challenges of AI in medical education, Chen envisions a future where automation and human judgment must be carefully balanced.
Latest News
from the Division
Physician-Reported Safety Outcomes of AI-Generated Hospital Course Summaries
A new JAMA Network Open study found that an AI-powered workflow for generating hospital discharge summaries was associated with reduced physician burnout and minimal reported safety risk during real-world clinical deployment. Authored by colleagues across DoM’s Divisions of Hospital Medicine and Computational Medicine, the Clinical Excellence Research Center, and Stanford colleagues.
Division & Research News
The Inverse Care Law in the Age of AI – Geographic Disparities in Health Care Technology Access
A new paper in NEJM AI highlights how AI may unintentionally deepen existing health disparities. First author Yeon-Mi Hwang, alongside senior author Tina Hernandez-Boussard, examines how rural communities — despite facing greater health burdens — have less access to the infrastructure needed to implement AI-driven care.
From AI Tool to Clinical Teammate
A new study led by Selin Everett, with senior authors Jonathan Chen & Eric Horvitz, explores how rethinking human-AI collaboration can improve clinical decision-making. Published in Nature Digital Medicine, the multicenter randomized controlled trial found that when physicians and AI reason independently and then combine their perspectives, diagnostic accuracy significantly improves.
Jonathan Chen on AI in Medicine: Promise, Pitfalls, and Practice
Jonathan Chen, MD, PhD, clinician, AI researcher, and Associate Professor at Stanford, whose work focuses on combining human and artificial intelligence to improve clinical decision-making. Dr. Chen reflects on the rapid rise of AI in medicine, and the moment he realized everything had changed. He also walks through surprising findings from his research, including studies showing that AI alone can sometimes outperform doctors using AI tools.
AI and the Future of Asian Health: Advancing Precision Medicine Through Human Insight and Technology
In this kickoff session, Dr. Jonathan H. Chen examines how human expertise and AI work together to improve diagnosis, prevention, and treatment—particularly in tackling health disparities, cultural nuances, and underrepresentation in data that impact Asian communities. He also emphasizes the importance of preserving empathy and human understanding at the core of patient care. Drawing on his experience as both a physician and biomedical informatics scientist, Dr. Chen shares how AI is making healthcare more efficient, personalized, and data-driven.
How a Winding Path Led to a Life-Saving Test
From uncertainty to impact, Purvesh Khatri’s career path was anything but straight. In this episode, he shares how curiosity and a willingness to pivot led to a breakthrough blood test that helps doctors make faster, life-saving decisions for sepsis.
Turning AI Promise into Real-World Practice
In conference rooms and clinics across the country, leaders are asking the same question: How do we move from excitement about artificial intelligence to systems that actually work — safely, responsibly, and at scale? At the Stanford Division of Computational Medicine, a new program aims to answer that question.
The Power of Research: How Discoveries Translate into Better Health
The latest issue of Stanford Medicine Magazine explores how discoveries move from the lab to lasting impact in patient care. We’re proud to share that several members of our DoM community are featured in this issue, including Alan Pao, Purvesh Khatri, Victoria Parikh, Crystal Mackall, Seung Kim, and more.
Uses of Generative AI by Non-Clinician Staff at an Academic Medical Center
A new study in npj Health Systems authored by Kameron Black, Stephen Ma, Hanna Kiani, Aditya Bhasin, Jonathan Chen, Nigam Shah, & colleagues, examines how non-clinician staff are using generative AI tools in real-world healthcare settings.
State of Clinical AI 2026: Inaugural Report Highlights Most Significant Developments and Challenges
The Stanford-Harvard AI Research and Science Evaluation (ARISE) network produced a comprehensive guide to help clinicians and decision-makers who are inundated with AI offerings. The report is presented as an easy-to-digest slide deck synthesizing the most impactful clinical AI research over the past year. Click to read the full report.
Biological data governance in an age of AI
As biological AI and general-purpose reasoning systems advance, the community faces balancing scientific progress with misuse risks. A new study by Tina Hernandez-Boussard, Russ Altman, & colleagues recommends narrow access controls on select high-risk pathogen data, while maintaining open access for most scientific data.