ArticleBMC medical informatics and decision making2025
Comparative evaluation of artificial intelligence models GPT-4 and GPT-3.5 in clinical decision-making in sports surgery and physiotherapy: a cross-sectional study.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of natural language processing and large language models in sports injury assessment and rehabilitation decision-making: a scoping review.Frontiers in medicine · 2026Pooled it
- Generative AI and Large Language Models in Rehabilitation: A Scoping Review.Life (Basel, Switzerland) · 2026Review
- Evaluating expert decision-pattern alignment in endovascular planning for intracranial aneurysms using a multimodal large language model.Neuroradiology · 2026Article
- Evaluating Large Language Models for AI-Assisted Decision Support in Legal Capacity Assessment: A Comparative Study Using Interdisciplinary Medical Board Recommendations as the Expert Medical Reference Standard.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- Evaluation of AI language models in answering pregnancy-related questions assessed by obstetrics specialists.Scientific reports · 2026Article
- Clinical reasoning with machines: evaluating the interpretive depth of AI in urological case assessments.BMC urology · 2026Observational
- Comparative Impact of ChatGPT and Conventional Search Tools on Clinical Reasoning Performance: A Randomized Crossover Study in Preclinical Medical Students.Advances in medical education and practice · 2026Article
- Can large language models follow guidelines? A comparative study of ChatGPT-4o and DeepSeek AI in clavicle fracture management based on AAOS recommendations.BMC medical informatics and decision making · 2025Article
- The impact of prompting on ChatGPT's adherence to status epilepticus treatment guidelines.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe integration of artificial intelligence (AI) in healthcare has rapidly expanded, particularly in clinical decision-making. Large language models (LLMs) such as GPT-4 and GPT-3.5 have shown potential in various medical applications, including diagnostics and treatment planning. However, their efficacy in specialized fields like sports surgery and physiotherapy remains underexplored. This study aims to compare the performance of GPT-4 and GPT-3.5 in clinical decision-making within these domains using a structured assessment approach.
methodsThis cross-sectional study included 56 professionals specializing in sports surgery and physiotherapy. Participants evaluated 10 standardized clinical scenarios generated by GPT-4 and GPT-3.5 using a 5-point Likert scale. The scenarios encompassed common musculoskeletal conditions, and assessments focused on diagnostic accuracy, treatment appropriateness, surgical technique detailing, and rehabilitation plan suitability. Data were collected anonymously via Google Forms. Statistical analysis included paired t-tests for direct model comparisons, one-way ANOVA to assess performance across multiple criteria, and Cronbach's alpha to evaluate inter-rater reliability.
resultsGPT-4 significantly outperformed GPT-3.5 across all evaluated criteria. Paired t-test results (t(55) = 10.45, p < 0.001) demonstrated that GPT-4 provided more accurate diagnoses, superior treatment plans, and more detailed surgical recommendations. ANOVA results confirmed the higher suitability of GPT-4 in treatment planning (F(1, 55) = 35.22, p < 0.001) and rehabilitation protocols (F(1, 55) = 32.10, p < 0.001). Cronbach's alpha values indicated higher internal consistency for GPT-4 (α = 0.478) compared to GPT-3.5 (α = 0.234), reflecting more reliable performance.
conclusionsGPT-4 demonstrates superior performance compared to GPT-3.5 in clinical decision-making for sports surgery and physiotherapy. These findings suggest that advanced AI models can aid in diagnostic accuracy, treatment planning, and rehabilitation strategies. However, AI should function as a decision-support tool rather than a substitute for expert clinical judgment. Future studies should explore the integration of AI into real-world clinical workflows, validate findings using larger datasets, and compare additional AI models beyond the GPT series.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.