ArticleDigital health
Performances of five large language models in clinical decision-making for internal medicine: A comparative study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Gut Instincts, Machine Decisions: Evaluating AI Accuracy in the Diagnosis and Treatment of Disorders of Gut-Brain Interaction.Neurogastroenterology and motility · 2026Article
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Authors and funding
3 authors.
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Abstract
Background: This study evaluated the performance of five Large Language Models based on actual cases to provide guidance for selecting appropriate models for clinical decision-making. Objective: This study aimed to assess the performance of large language models (LLMs) in clinical decision-making for internal medicine and to provide evidence-based guidance for model selection in clinical practice. Methods: We conducted a retrospective cross-sectional study with 405 cases across nine subspecialties: cardiovascular, respiratory, gastroenterology, nephrology, rheumatology, endocrinology, neurology, hematology, and infectious diseases. Two senior clinicians evaluated outputs on five dimensions: diagnosis, diagnostic criteria, differential diagnosis, examinations, and treatment. Statistical analyses were performed via the Kruskal‒Wallis tests and Pairwise comparisons were performed by Dunn's test with p-value adjusted by BH procedure. Results: Overall, significant performance differences were observed among models ( Conclusion: GPT, O1, and Gemini demonstrated superior performance in clinical decision-making for internal medicine among all LLMs, whereas Claude showed the poorest performance. All LLMs demonstrated deficiencies in differential diagnosis and poor management for respiratory diseases. The complexity of subspecialty might be a performance differentiator for LLMs and O1 might have potential suitability for complex subspecialties like cardiology.
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