Academic Bio: Prof. Pranav Rajpurkar is Assistant Professor of Biomedical Informatics at Harvard Medical School and Co-founder of a2z Radiology AI. A Stanford-trained computer scientist, his research focuses on developing AI systems that match physician-level expertise through multi-modal clinical data interpretation. At Harvard, his lab develops frameworks for evaluating and advancing medical AI in clinical language models and medical imaging. His research includes over 100 publications in journals like Nature, NEJM, and Nature Medicine, garnering over 35,000 citations. Prof. Rajpurkar has educated over 84,000 students through his AI in Medicine courses at Harvard and Coursera. Named among MIT Tech Review's Innovators Under 35 (2023), Forbes 30 Under 30 in Science (2022), and Nature Medicine's Early-career Researchers To Watch (2022), he continues to advance the integration of AI in clinical practice.
Commercial Bio: Dr. Pranav Rajpurkar is the Co-founder of a2z Radiology AI, developing comprehensive AI systems for diagnostic imaging that can analyze hundreds of clinical findings in each scan—far beyond the narrow scope of current tools. A Stanford-trained computer scientist and Assistant Professor at Harvard Medical School, he leads research on AI systems that think and communicate like doctors. His work, including over 100 research papers, has been featured in The New York Times, CBS, NPR, and Financial Times, and he has been recognized as an MIT Tech Review Innovator Under 35 (2023), Forbes 30 Under 30 in Science (2022), and Nature Medicine's Early-career Researchers To Watch (2022). With over 35,000 citations to his research, Dr. Rajpurkar works toward making expert medical care more accessible through AI.
Mini Bio: Dr. Pranav Rajpurkar is Co-founder of a2z Radiology AI and Assistant Professor at Harvard Medical School. A Stanford-trained computer scientist, he develops AI systems for comprehensive medical decision making. His 100 plus academic research papers have garnered over 35,000 citations and recognition from MIT Tech Review, Forbes 30 Under 30, and Nature Medicine.
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Academic talk title and abstract
Beyond Assistance: Rethinking AI-Human Integration in Radiology
Recent evidence challenges a fundamental assumption in medical AI: that combining AI with physician expertise naturally leads to better outcomes. Studies show that AI assistance often fails to improve diagnostic accuracy and can even slow down clinical workflows. This talk presents an alternative vision: instead of forcing integration, we should embrace clear role separation between AI systems and physicians. Drawing from recent large-scale studies and advances in generalist medical AI systems, I will examine promising models where AI and doctors work separately but complementarily, each leveraging their unique strengths. Through practical examples and emerging evidence, I will demonstrate how this approach could transform clinical practice while maintaining the essential role of human medical expertise.