Stanford University
This study evaluates the impact of large language models (LLMs) versus traditional decision support tools on clinical decision-making in cardiology. General cardiologists will be randomized to manage real patient cases from a cardiovascular genetic cardiomyopathy clinic, with or without AI assistance. Each case will be assessed by two cardiologists, and their responses will be graded by blinded subspecialty experts using a standardized evaluation rubric.
Large language models have been shown to improve physician performance in simulated settings. Large language models have demonstrated promise in various healthcare contexts, including medical note-writing, addressing patient inquiries, and facilitating medical consultation. However, it remains uncertain whether large language models improve clinical reasoning of clinicians using real world cases. Clinicians dedicate years of training to develop expertise, with clinical knowledge a key component. Clinicians have different areas of expertise, from generalists spanning diseases of all organ syst…
Inclusion Criteria: * Board certified or board eligible Cardiologist. Exclusion Criteria: * Not currently practicing clinically
The intervention is a Large Language Model.
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