Comparing Artificial Intelligence–Generated and Clinician-Created Personalized Self-Management Guidance for Patients With Knee Osteoarthritis: Blinded Observational Study

GPT-4 could generate personalized self-management guidance for knee osteoarthritis with greater efficiency, accuracy, personalization, comprehensiveness, and safety than clinician-generated content, as assessed using standardized, guideline-aligned evaluation frameworks. These findings underscore the potential of large language models to support scalable, high-quality patient education in chronic disease management. The observed lexical complexity suggests the need to refine outputs for populations with limited health literacy. As an exploratory, single-center study, these results warrant confirmation in larger, multicenter cohorts with diverse demographic profiles. Future implementation should be guided by ethical and operational safeguards, including data privacy, transparency, and the delineation of clinical responsibility. Hybrid models integrating artificial intelligence-generated content with clinician oversight may offer a pragmatic path forward.

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Publication Details

Journal
Journal of Medical Internet Research
Published
2025-03-31
DOI
https://doi.org/10.2196/67830
Citations
10
Primary Topic
Osteoarthritis Treatment and Mechanisms
Type
article
Field-Weighted Citation Impact
16.68
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article

Comparing Artificial Intelligence–Generated and Clinician-Created Personalized Self-Management Guidance for Patients With Knee Osteoarthritis: Blinded Observational Study

Wei-Shuai Du, Qi Zuo, Chenyu Zhang, Ping Chen et al.
10 citations
Journal of Medical Internet Research
Osteoarthritis Treatment and Mechanisms
16.68
article

Comparing Artificial Intelligence–Generated and Clinician-Created Personalized Self-Management Guidance for Patients With Knee Osteoarthritis: Blinded Observational Study

Wei-Shuai Du, Qi Zuo, Chenyu Zhang, Ping Chen, Shu-Ming Li, Ao Li, Ren Guo, Kai Du
article en
10 citations

Abstract

GPT-4 could generate personalized self-management guidance for knee osteoarthritis with greater efficiency, accuracy, personalization, comprehensiveness, and safety than clinician-generated content, as assessed using standardized, guideline-aligned evaluation frameworks. These findings underscore the potential of large language models to support scalable, high-quality patient education in chronic disease management. The observed lexical complexity suggests the need to refine outputs for populations with limited health literacy. As an exploratory, single-center study, these results warrant confirmation in larger, multicenter cohorts with diverse demographic profiles. Future implementation should be guided by ethical and operational safeguards, including data privacy, transparency, and the delineation of clinical responsibility. Hybrid models integrating artificial intelligence-generated content with clinician oversight may offer a pragmatic path forward.

Journal of Medical Internet ResearchVol. 27
Beijing University of Chinese Medicine (CN), Beijing Hospital of Traditional Chinese Medicine (CN)
Openalex Percentile: Top 1%
Osteoarthritis Treatment and Mechanisms
16.68
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Comparing Artificial Intelligence–Generated and Clinician-Created Personalized Self-Management Guidance for Patients With Knee Osteoarthritis: Blinded Observational Study — Wei-Shuai Du, Qi Zuo, et al. · Journal of Medical Internet Research (2025) | TGRS Research Map | TGRS