Artificial Intelligence in Physical Therapy: Evaluating ChatGPT's Role in Clinical Decision Support for Musculoskeletal Care.

IF 3 2区医学 Q3 ENGINEERING, BIOMEDICAL

Annals of Biomedical Engineering Pub Date : 2025-01-01 Epub Date: 2025-01-06 DOI:10.1007/s10439-025-03676-4

Jie Hao, Zixuan Yao, Yaogeng Tang, Andréas Remis, Kangchao Wu, Xin Yu

{"title":"Artificial Intelligence in Physical Therapy: Evaluating ChatGPT's Role in Clinical Decision Support for Musculoskeletal Care.","authors":"Jie Hao, Zixuan Yao, Yaogeng Tang, Andréas Remis, Kangchao Wu, Xin Yu","doi":"10.1007/s10439-025-03676-4","DOIUrl":null,"url":null,"abstract":"Background: The integration of artificial intelligence into medicine has attracted increasing attention in recent years. ChatGPT has emerged as a promising tool for delivering evidence-based recommendations in various clinical domains. However, the application of ChatGPT to physical therapy for musculoskeletal conditions has yet to be investigated.Methods: Thirty clinical questions related to spinal, lower extremity, and upper extremity conditions were quired to ChatGPT-4. Responses were assessed for accuracy against clinical practice guidelines by two reviewers. Intra- and inter-rater reliability were measured using Fleiss' kappa (k).Results: ChatGPT's responses were consistent with CPG recommendations for 80% of the questions. Performance was highest for upper extremity conditions (100%) and lowest for spinal conditions (60%), with a moderate performance for lower extremity conditions (87%). Intra-rater reliability was good (k = 0.698 and k = 0.631 for the two reviewers), and inter-rater reliability was very good (k = 0.847).Conclusion: ChatGPT demonstrates promise as a supplementary decision-making support tool for physical therapy, with good accuracy and reliability in aligning with clinical practice guideline recommendations. Further research is needed to evaluate its performance across broader scenarios and refine its clinical applicability.","PeriodicalId":7986,"journal":{"name":"Annals of Biomedical Engineering","volume":" ","pages":"9-13"},"PeriodicalIF":3.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10439-025-03676-4","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/6 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Background: The integration of artificial intelligence into medicine has attracted increasing attention in recent years. ChatGPT has emerged as a promising tool for delivering evidence-based recommendations in various clinical domains. However, the application of ChatGPT to physical therapy for musculoskeletal conditions has yet to be investigated.

Methods: Thirty clinical questions related to spinal, lower extremity, and upper extremity conditions were quired to ChatGPT-4. Responses were assessed for accuracy against clinical practice guidelines by two reviewers. Intra- and inter-rater reliability were measured using Fleiss' kappa (k).

Results: ChatGPT's responses were consistent with CPG recommendations for 80% of the questions. Performance was highest for upper extremity conditions (100%) and lowest for spinal conditions (60%), with a moderate performance for lower extremity conditions (87%). Intra-rater reliability was good (k = 0.698 and k = 0.631 for the two reviewers), and inter-rater reliability was very good (k = 0.847).

Conclusion: ChatGPT demonstrates promise as a supplementary decision-making support tool for physical therapy, with good accuracy and reliability in aligning with clinical practice guideline recommendations. Further research is needed to evaluate its performance across broader scenarios and refine its clinical applicability.

查看原文本刊更多论文

物理治疗中的人工智能：评估ChatGPT在肌肉骨骼护理临床决策支持中的作用。

背景：近年来，人工智能与医学的融合越来越受到关注。ChatGPT已成为在各种临床领域提供循证建议的有前途的工具。然而，ChatGPT在肌肉骨骼疾病物理治疗中的应用还有待研究。方法：在ChatGPT-4中填写与脊柱、下肢和上肢状况相关的30个临床问题。根据临床实践指南，由两名审稿人评估反应的准确性。使用Fleiss kappa (k)测量内部和内部的信度。结果：ChatGPT的回答在80%的问题上与CPG的建议一致。上肢疾病的表现最高（100%），脊柱疾病的表现最低（60%），下肢疾病的表现中等（87%）。评价者内部信度较好（k = 0.698, k = 0.631），评价者之间信度很好（k = 0.847）。结论：ChatGPT作为物理治疗辅助决策支持工具，具有良好的准确性和可靠性，符合临床实践指南建议。需要进一步的研究来评估其在更广泛的情况下的表现并完善其临床适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Annals of Biomedical Engineering 工程技术-工程：生物医学

CiteScore

7.50

自引率

15.80%

发文量

212

审稿时长

3 months

期刊介绍： Annals of Biomedical Engineering is an official journal of the Biomedical Engineering Society, publishing original articles in the major fields of bioengineering and biomedical engineering. The Annals is an interdisciplinary and international journal with the aim to highlight integrated approaches to the solutions of biological and biomedical problems.