ArticleInternational journal of emergency medicine2025
Evaluating large language models for renal colic imaging recommendations: a comparative analysis of Gemini, copilot, and ChatGPT-4.0.
Article in International journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe field of natural language processing (NLP) has evolved significantly since its inception in the 1950s, with large language models (LLMs) now playing a crucial role in addressing medical challenges.
objectivesThis study evaluates the alignment of three prominent LLMs-Gemini, Copilot, and ChatGPT-4.0-with expert consensus on imaging recommendations for acute flank pain.
methodsA total of 29 clinical vignettes representing different combinations of age, sex, pregnancy status, likelihood of stone disease, and alternative diagnoses were posed to the three LLMs (Gemini, Copilot, and ChatGPT-4.0) between March and April 2024. Responses were compared to the consensus recommendations of a multispecialty panel. The primary outcome was the rate of LLM responses matching the majority consensus. Secondary outcomes included alignment with consensus-rated perfect (9/9) or excellent (8/9) responses and agreement with any of the nine panel members.
resultsGemini aligned with the majority consensus in 65.5% of cases, compared to 41.4% for both Copilot and ChatGPT-4.0. In scenarios rated as perfect or excellent by the consensus, Gemini showed 69.5% agreement, significantly higher than Copilot and ChatGPT-4.0, both at 43.4% (p = 0.045 and < 0.001, respectively). Overall, Gemini demonstrated an agreement rate of 82.7% with any of the nine reviewers, indicating superior capability in addressing complex medical inquiries.
conclusionGemini consistently outperformed Copilot and ChatGPT-4.0 in aligning with expert consensus, suggesting its potential as a reliable tool in clinical decision-making. Further research is needed to enhance the reliability and accuracy of LLMs and to address the ethical and legal challenges associated with their integration into healthcare systems.
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