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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
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.
AI Outperformed Doctors on Diagnosing Touch Cases – But is it Ready for Real Patients?
An AI program put to the most rigorous tests in modern medicine aced its exams, and in fact performed better than human doctors on reasoning tasks such as making emergency room decisions.
New Study Sheds Light on Growing Capabilities of AI in Medicine
One of the first studies conducted on AI’s ability to perform complex medical reasoning tasks has been published in “Science” magazine on Thursday. It has found that AI is often performing better than human physicians in complex medical diagnosis and reasoning.
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.
Performance of a Large Language Model on the Reasoning Tasks of a Physician
In a new Science study, Ethan Goh, Evelyn Bin Ling, Jason Hom, Jonathan Chen, and colleagues from Harvard Medical School and Beth Israel Deaconess Medical Center evaluated the OpenAI o1 model against hundreds of physicians across real-world clinical scenarios. The model outperformed both clinicians and prior systems in diagnosis and management, underscoring its growing potential in clinical care.
Nearly Half of Americans Use AI to Help Make Health Care Decisions, Poll Finds
A new Gallup poll finds nearly half of U.S. adults are using artificial intelligence to help make health decisions, with some turning to it instead of seeing a doctor. An estimated 14 million Americans say they’ve skipped medical care altogether based on AI guidance. Dr. Nigam Shah, the chief data scientist at Stanford Health Care, joined us on ‘The Nine’ to break down the risks, benefits and what patients should keep in mind.
Nigam Shah and Paul Bollyky Elected to AAP
Congratulations to Nigam Shah and Paul Bollyky on their election to the Association of American Physicians (AAP). They were recognized among colleagues at the AAP New Member Induction Dinner over the weekend — an honor that highlights their outstanding contributions to academic medicine and research.
Turning AI Promise into Real-World Practice: Stanford AI in Healthcare Leadership and Strategy
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 $1 Trillion Problem AI Still Can’t Yet Solve
The invisible layer of healthcare, known as the administrative spending, costs the United States more than $1 trillion every year. Almost 25 cents of every dollar in healthcare. Despite all the excitement around AI, administrative spending remains largely untouched.
*Artwork courtesy of Jennie Ellison*
AI Translation in Healthcare: An Urgent Call For Evidence-Informed Policy Frameworks
In a new BMJ perspective, researchers Chuk Anyaegbuna, Natasha Steele, April Shichu Liang, Stephen Ma, Ivan Lopez, Nymisha Chilukuri, Kavita Patel, Kevin Schulman, & Jonathan Chen warn that AI translation tools are rapidly entering healthcare without adequate oversight — raising risks of uneven performance across languages and widening health disparities.