Kevin A. Schulman on Muck Rack

Kevin A. Schulman

Washington, D.C.
As seen in: Nature, Wiley Online Library, Los Angeles Times, Stanford University, Harvard Business Review, The Hill, JAMA (The Journal of the American Medical Association), JAMA Internal Medicine Author Interviews, JAMA Network, JAMA: Journal of the American Medical Association and
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Articles

Private Equity and the Safety Net—Wolf in Sheep’s Clothing

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By Dashiell S. Miner, Kevin A. Schulman, George Nageeb
| JAMA Network Verified
Private equity investment in not-for-profit hospitals has had a mixed track record. Private investors offer capital and management expertise to distressed institutions with resources often not readily available. However, public oversight of these transactions and ongoing financial management is generally very limited.
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From tool to teammate in a randomized controlled trial of clinician-AI collaborative workflows for diagnosis

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By Selin S Everett, Bryan Bunning, Priyank Jain, Ivan Lopez, Ivan López, Iván López, Anup Agarwal, Manisha Desai, Robert J. Gallo, Ethan Goh, Zahir Kanjee, Jacob Koshy Verified, Andrew Olson, Adam Rodman, Kevin A. Schulman, Eric Strong, Jonathan Chen, Eric J Horvitz, Vinay B. Kadiyala
| Nature Verified
Abstract Early studies of large language models (LLMs) in clinical settings have largely treated artificial intelligence (AI) as a tool rather than an active collaborator. As LLMs demonstrate expert-level diagnostic performance, the focus shifts from whether AI can offer valuable suggestions to how it integrates into physicians’ diagnostic workflows. We conducted a randomized controlled trial (n = 70 clinicians) to assess a custom system designed for collaborative diagnostic reasoning.
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Holistic evaluation of large language models for medical tasks with MedHELM - Nature Medicine

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By Suhana Bedi, Miguel Fuentes, Juan M. Banda, Timothy J. Keyes, Yifan Mai, Jason Alan Fries, Philip Chung, Fateme Nateghi Haredasht, Ivan Lopez, Ivan López, Iván López, Shivam Vedak, Sneha Jain, Ethan Goh, Robson Capasso, Jenelle Jindal, Faraz Ghoddusi, Albert S. Chiou, Michael Gensheimer, Hinesh Patel, Kevin A. Schulman, Kameron Collin Black, Harshita Sharma, Aditya Sharma, Yair Bannett, Roxana Daneshjou, Jonathan Chen, Nirmal Ravi, Nima Aghaeepour, Akshay S. Chaudhari, Thomas J. Wang, Sanmi Koyejo, Eric J Horvitz, Michael Pfeffer, Leonardo Schettini, Asad Aali, Nerissa Ambers
| Nature Verified
Abstract While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinical practice. Here we introduce MedHELM, an extensible evaluation framework with three contributions.
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