ArticleFrontiers in sports and active living2026
A comparative study of four large language models in treatment decision-making for Hip impingement syndrome based on real-world data.
Article in Frontiers in sports and active living, 2026. 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
Background: Large language models (LLMs) hold potential value in medical decision support, yet empirical studies systematically comparing multiple models for femoroacetabular impingement (FAI) within Chinese clinical contexts remain scarce. Objective: This study aimed to compare the classification performance, decision consistency, and decision confidence of four mainstream LLMs (GPT-5, Gemini 2.5 Pro, DeepSeek-R1, Grok-4) in FAI treatment decision-making tasks for FAI based on real-world hospitalized cases, and to evaluate their clinical adaptability and limitations. Methods: We retrospectively included 26 hospitalized FAI cases from our institution (13 surgical group, 13 conservative group), all of which had ethically approved and informed consented. Case information was standardized into two input formats: structured radiology reports only (Group A), and structured radiology reports combined with structured medical records-referred to as multi-source structured text input in this study (Group B). Under both conditions, four LLMs were tasked with providing binary treatment decisions ("surgical" or "conservative") and indicating decision confidence on a scale from 0 to 100%. Primary evaluation metrics included accuracy, precision, sensitivity, specificity, F Results: Under Group B conditions (incorporating structured medical record information), GPT-5 demonstrated optimal performance, with accuracy of 88%, precision of 92%, sensitivity of 85%, specificity of 92%, F Conclusion: In this exploratory, retrospective single-center study with a small sample size, GPT-5 outperformed the other evaluated models in FAI treatment decision support tasks based on Chinese structured clinical information and demonstrated, to some extent, calibrated decision confidence. These preliminary findings suggest the feasibility of LLMs in orthopedic decision support, however, validation in larger, multi-center, prospective studies is required before broader clinical application. Future studies should adopt multi-center, prospective designs to enhance evaluations of model reproducibility, interpretability, and clinical safety, thereby further validating broader applicability.
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