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Arjun K. Manrai on Muck Rack

Arjun K. Manrai

Boston
As seen in: Harvard Medical Labcast, NEJM AI Grand Rounds, Nature, Stanford University, Flipboard, JAMA (The Journal of the American Medical Association), JAMA Network, JAMA: Journal of the American Medical Association, The Boston Globe, The New England Journal of Medicine, STAT, International Journal of Epidemiology and
Associate Professor of Biomedical Informatics, Harvard Medical School @HarvardDBMI | Founding Deputy Editor, @NEJM_AI | Co-host, NEJM AI Grand Rounds
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Articles

Medical AI has a measurement problem

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By Arjun K. Manrai
| Nature Verified
NEWS AND VIEWS 28 July 2026 The development of two medical AI assistants highlights an unnerving challenge: as the technology races ahead, what is the best way to evaluate what works? Evidence-based medicine rests on the assumption that the yardstick used to measure the effectiveness of a new drug or medical device is reliable. But this assumption is often shaky, especially in the context of the artificial-intelligence tools that are making their way into clinical practice.
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Toward a test of medical AI superintelligence

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By Ethan Goh, David Wu Verified, Chase Walton, Liam McCoy, Anastasia Perez, Laura Wegner, Fateme Nateghi Haredasht, Luyang Luo, Thomas Buckley Verified, Austin Schoeffler, Peter G. Brodeur, John Havlik, Yun Liu, Po-Hsuan Cameron Chen, Mike Schaekermann, John S. Rumsfeld, Daniel Lopez-Martinez, Paxton Maeder-York, karan singhal, David Gunning, Bon Ku, Haider Warraich, Shantanu Nundy, Vishnu Ravi, Arnold Milstein, Jason Hom, Kevin A. Schulman, Pranav Rajpurkar, Arjun K. Manrai, Robert Wachter, Eric Jeffrey Topol, Eric J Horvitz, Adam Rodman, Jonathan Chen, Kathleen Lacar, Kameron C. Black
| Nature Verified
Researchers urgently need a rigorous, task-based framework to define and measure medical AI ‘superintelligence’, because existing benchmarks are misleading and insufficient.
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Screening for Missed Opportunities for Diagnosis in the ED Using eTriggers and Large Language Models

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By Clifford M. Marks, Bryan A. Stenson, Deesha Sarma, Cynthia Gaudet, Thomas Buckley Verified, Mario Keko, Larry A. Nathanson, Laura G. Burke, Nathan I Shapiro, Shamai Grossman, Alexander T. Janke, Arjun K. Manrai, Richard Taylor, Carlo L. Rosen, Adam Rodman, Adrian D. Haimovich, Sean Gibney, Haadi Mombini, Jonathan L. Burstein, Anika Parab
| JAMA Network Verified
Screening for Missed Opportunities for Diagnosis in the ED Using eTriggers and Large Language Models Supplement 1. eTable 1. Strata demographics in 72-hour return cohort: Academic medical center vs network eTable 2. Full confusion matrix for all LLMs eTable 3. All model performance findings with 95% confidence intervals eTable 4. Pairwise DeLong comparisons of the highest-observed AUC model versus the remaining models within each cohort eFigure 1.
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