Superhuman performance of a large language model on the reasoning tasks of a physician
Peter G. Brodeur, Thomas A. Buckley, Zahir Kanjee, Ethan Goh, Evelyn Bin Ling, Priyank Jain, Stephanie Cabral, Raja-Elie Abdulnour, Adrian D. Haimovich, Jason A. Freed, Andrew Olson, Daniel J. Morgan, Jason Hom, Robert Gallo, Liam G. McCoy, Haadi Mombini, Christopher Lucas

TL;DR
This study demonstrates that a large language model can outperform human physicians in complex clinical reasoning tasks and real-world emergency room scenarios, indicating a breakthrough in AI-assisted medical diagnosis.
Contribution
The paper provides empirical evidence of superhuman performance of an LLM on diverse clinical reasoning tasks and real-world emergency room evaluations, surpassing prior AI systems.
Findings
LLM outperforms physicians in diagnostic reasoning tasks
LLM shows continued improvement over previous AI models
In emergency settings, LLM matches or exceeds physician accuracy
Abstract
A seminal paper published by Ledley and Lusted in 1959 introduced complex clinical diagnostic reasoning cases as the gold standard for the evaluation of expert medical computing systems, a standard that has held ever since. Here, we report the results of a physician evaluation of a large language model (LLM) on challenging clinical cases against a baseline of hundreds of physicians. We conduct five experiments to measure clinical reasoning across differential diagnosis generation, display of diagnostic reasoning, triage differential diagnosis, probabilistic reasoning, and management reasoning, all adjudicated by physician experts with validated psychometrics. We then report a real-world study comparing human expert and AI second opinions in randomly-selected patients in the emergency room of a major tertiary academic medical center in Boston, MA. We compared LLMs and board-certified…
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Taxonomy
TopicsClinical Reasoning and Diagnostic Skills · Artificial Intelligence in Healthcare and Education
