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Pranav Rajpurkar is a computer scientist and Associate Professor of Biomedical Informatics at Harvard Medical School. He leads the Rajpurkar Lab and co-founded a2z Radiology AI. His research develops and evaluates artificial intelligence for medicine.
Pranav Rajpurkar is a computer scientist and Associate Professor of Biomedical Informatics at Harvard Medical School. He co-founded a2z Radiology AI. He received his BS, MS, and PhD degrees in computer science from Stanford University.
He leads the Rajpurkar Lab, which builds medical AI for complex clinical work: AI that reasons from evidence, methods to test it before it is trusted, and robots that learn to assist in procedures. His research asks how these systems can move beyond benchmark performance to become reliable and useful in healthcare.
Pranav Rajpurkar is a computer scientist and Associate Professor of Biomedical Informatics at Harvard Medical School. He received his BS, MS, and PhD degrees in computer science from Stanford University.
At Harvard, the Rajpurkar Lab builds medical AI for complex clinical work across three programs: AI that reasons from evidence, methods to test it before it is trusted, and robots that learn to assist in procedures.
Dr. Rajpurkar has authored more than 200 research publications, which have received 58,000+ citations according to Google Scholar. His work appears in Nature, Nature Medicine, Cell, The Lancet, the New England Journal of Medicine, and other medical and computing venues. He also co-authored The Robot Doctor Will See You Now with Eric Topol for The New York Times.
He co-founded a2z Radiology AI. Its two FDA-cleared devices, a2z-Abdo-Triage and a2z-Unified-Triage, flag suspected cases across 17 findings on adult abdomen and pelvis CT for worklist prioritization.
Pranav Rajpurkar is a computer scientist and Associate Professor of Biomedical Informatics at Harvard Medical School. He received his BS, MS, and PhD degrees in computer science from Stanford University.
The Rajpurkar Lab builds medical AI for complex clinical work across three programs. Reason from evidence: generalist models that read and write across images and text and tie each finding to the evidence behind it. Test before we trust: public benchmarks, simulated patients, studies across many hospitals, and simulated hospitals that show whether AI is ready before it meets a patient. Learn to assist: robots that learn from expert demonstration to assist clinicians in the operating room and at the bedside.
The goal is to measure what these systems can and cannot do, then develop methods that make them more reliable and useful in healthcare.
Dr. Rajpurkar has authored more than 200 research publications, which have received 58,000+ citations according to Google Scholar. His work appears in Nature, Nature Medicine, Cell, The Lancet, the New England Journal of Medicine, and other medical and computing venues. He also co-authored The Robot Doctor Will See You Now with Eric Topol for The New York Times.
Dr. Rajpurkar co-founded a2z Radiology AI. The company’s two FDA-cleared devices, a2z-Abdo-Triage and a2z-Unified-Triage, analyze adult abdomen and pelvis CT studies and flag suspected cases across 17 findings for worklist prioritization.
Recognition includes MIT Technology Review’s Innovators Under 35, Forbes 30 Under 30 in Science, Nature Medicine’s early-career researchers to watch, and a 2026 ACL Test-of-Time Paper Award for the 2016 SQuAD paper. Dr. Rajpurkar is an instructor for the three-course AI for Medicine series, whose courses have reached 90,000+ learners on Coursera.