{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Large Language Model Influence on Diagnostic Reasoning","author":[{"@type":"Person","name":"Ethan Goh"},{"@type":"Person","name":"Robert Gallo"},{"@type":"Person","name":"Jason Hom"},{"@type":"Person","name":"Eric Strong"},{"@type":"Person","name":"Yingjie Weng"},{"@type":"Person","name":"Hannah Kerman"},{"@type":"Person","name":"Joséphine A. Cool"},{"@type":"Person","name":"Zahir Kanjee"},{"@type":"Person","name":"Andrew S. Parsons"},{"@type":"Person","name":"Neera Ahuja"},{"@type":"Person","name":"Eric Horvitz"},{"@type":"Person","name":"Daniel X. Yang"},{"@type":"Person","name":"Arnold Milstein"},{"@type":"Person","name":"Andrew Olson"},{"@type":"Person","name":"Adam Rodman"},{"@type":"Person","name":"Jonathan H. Chen"}],"datePublished":"2024","isPartOf":{"@type":"Periodical","name":"JAMA Network Open"},"identifier":"https://doi.org/https://doi.org/10.1001/jamanetworkopen.2024.40969","description":"ImportanceLarge language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning.ObjectiveTo assess the effect of an LLM on physicians’ diagnostic reasoning compared with conventional resources.Design, Setting, and ParticipantsA single-blind randomized clinical trial was conducted from November 29 to December 29, 2023. Using remote video conferencing and in-person participation across multiple academic medical institutions, physicians with training in family medicine, internal medicine, or emergency medicine were recruited.InterventionParticipants were randomized to either access the LLM in addition to conventional diagnostic resources or conventional resources only, stratified by career stage. Participants were allocated 60 minutes to review up to 6 clinical vignettes.Main Outcomes and MeasuresThe primary outcome was performance on a standardized rubric of diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps, validated and graded via blinded expert consensus. Secondary outcomes included time spent per case (in seconds) and final diagnosis accuracy. All analyses followed the intention-to-treat principle. A secondary exploratory analysis evaluated the standalone performance of the LLM by comparing the primary outcomes between the LLM alone group and the conventional resource group.ResultsFifty physicians (26 attendings, 24 residents; median years in practice, 3 [IQR, 2-8]) participated virtually as well as at 1 in-person site. The median diagnostic reasoning score per case was 76% (IQR, 66%-87%) for the LLM group and 74% (IQR, 63%-84%) for the conventional resources-only group, with an adjusted difference of 2 percentage points (95% CI, −4 to 8 percentage points; P = .60). The median time spent per case for the LLM group was 519 (IQR, 371-668) seconds, compared with 565 (IQR, 456-788) seconds for the conventional resources group, with a time difference of −82 (95% CI, −195 to 31; P = .20) seconds. The LLM alone scored 16 percentage points (95% CI, 2-30 percentage points; P = .03) higher than the conventional resources group.Conclusions and RelevanceIn this trial, the availability of an LLM to physicians as a diagnostic aid did not significantly improve clinical reasoning compared with conventional resources. The LLM alone demonstrated higher performance than both physician groups, indicating the need for technology and workforce development to realize the potential of physician-artificial intelligence collaboration in clinical practice.Trial RegistrationClinicalTrials.gov Identifier: NCT06157944","url":"https://innscience.ca/es/community/literature/1f9e6c8f-0130-460a-ba4d-89b03c3a4404"}