Assessing the accuracy, safety, and clinical utility of AI-generated rehabilitation guidance for Bankart lesions: a blinded comparative evaluation with expert protocols

Aims: Artificial Intelligence (AI)–based language models are increasingly used to generate medical information and patient education materials. However, the reliability and safety of AI-generated rehabilitation guidance remain uncertain. This study aimed to evaluate the accuracy, safety, clinical utility, and readability of rehabilitation recommendations generated by ChatGPT-5 for Bankart lesions and to compare these outputs with expert-developed rehabilitation protocols.Methods: A blinded, cross-sectional comparative quality assessment was conducted. Standardized prompts regarding nonoperative and postoperative Bankart rehabilitation were used to generate responses from ChatGPT-5. AI-generated texts were compared with protocols prepared by a panel of orthopedic shoulder surgeons and an experienced physiotherapist. All texts were anonymized and independently evaluated by three blinded expert raters using a structured 5-point Likert scale assessing clinical accuracy, safety, actionability, comprehensiveness, and overall quality. Major clinical errors were recorded separately. Readability was assessed using Flesch Reading Ease and Flesch–Kincaid Grade Level scores. Inter-rater reliability was analyzed using intraclass correlation coefficients (ICC). Results: A total of 20 rehabilitation texts (10 AI-generated and 10 expert-developed) were evaluated. Expert protocols demonstrated significantly higher scores in clinical accuracy (4.6±0.4 vs 3.4±0.7, p

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Publication Details

Journal
Journal of Medicine and Palliative Care
Published
2026-03-27
DOI
https://doi.org/10.47582/jompac.1884791
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

Assessing the accuracy, safety, and clinical utility of AI-generated rehabilitation guidance for Bankart lesions: a blinded comparative evaluation with expert protocols

Ali Can Koluman, Cemal Kural, Nezih Ziroğlu, Mehmet Utku Çiftçi et al.
Journal of Medicine and Palliative Care
Artificial Intelligence in Healthcare and Education
article

Assessing the accuracy, safety, and clinical utility of AI-generated rehabilitation guidance for Bankart lesions: a blinded comparative evaluation with expert protocols

Ali Can Koluman, Cemal Kural, Nezih Ziroğlu, Mehmet Utku Çiftçi, Ahmet Yiğitbay, E. Çiftçi
article en

Abstract

Aims: Artificial Intelligence (AI)–based language models are increasingly used to generate medical information and patient education materials. However, the reliability and safety of AI-generated rehabilitation guidance remain uncertain. This study aimed to evaluate the accuracy, safety, clinical utility, and readability of rehabilitation recommendations generated by ChatGPT-5 for Bankart lesions and to compare these outputs with expert-developed rehabilitation protocols.Methods: A blinded, cross-sectional comparative quality assessment was conducted. Standardized prompts regarding nonoperative and postoperative Bankart rehabilitation were used to generate responses from ChatGPT-5. AI-generated texts were compared with protocols prepared by a panel of orthopedic shoulder surgeons and an experienced physiotherapist. All texts were anonymized and independently evaluated by three blinded expert raters using a structured 5-point Likert scale assessing clinical accuracy, safety, actionability, comprehensiveness, and overall quality. Major clinical errors were recorded separately. Readability was assessed using Flesch Reading Ease and Flesch–Kincaid Grade Level scores. Inter-rater reliability was analyzed using intraclass correlation coefficients (ICC). Results: A total of 20 rehabilitation texts (10 AI-generated and 10 expert-developed) were evaluated. Expert protocols demonstrated significantly higher scores in clinical accuracy (4.6±0.4 vs 3.4±0.7, p

Journal of Medicine and Palliative CareVol. 7(2)
Suleyman Demirel University Research and Education Hospital (TR), Sivas State Hospital (TR), Bakırköy Dr.Sadi Konuk Eğitim ve Araştırma Hastanesi (TR), Adli Tıp Kurumu (TR), University of Health Sciences (SO), Okan University (TR)
Quality Education
Openalex Percentile: Top 5%
Artificial Intelligence in Healthcare and Education
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