ChatGPT provides accurate and safe responses to patient questions on hip arthroscopy, while completeness remains variable: A systematic review and single‐arm meta‐analysis

PURPOSE: Large language models, such as ChatGPT, are increasingly used by patients seeking information on hip arthroscopy (HAS) and femoroacetabular impingement (FAI). Despite their linguistic fluency, the accuracy, completeness and safety of procedure-specific patient information remain unclear. Although orthopaedic studies report variable performance across subspecialties, no systematic evaluation has specifically addressed HAS. METHODS: PubMed, Embase, Scopus, CINAHL and Epistemonikos were searched to 10 February 2026 for studies evaluating ChatGPT responses to patient-oriented HAS or FAI questions. Randomized and non-randomized studies, observational cohorts and case series were eligible. Data on question sources, model versions, rating systems and performance domains (accuracy, relevance, completeness, safety, readability and clarity) were extracted. Heterogeneous rating scales were dichotomized into high- versus low-quality responses. Risk of bias was assessed using QUADAS-2 and ROBINS-I. Random-effects single-arm meta-analyses (REML) were conducted for each domain. RESULTS: Eight studies met eligibility criteria. Accuracy was high (pooled 88.6%). Relevance, safety, readability and clarity reached pooled values of 100% with low heterogeneity. Completeness was lower (83.8%) with moderate heterogeneity, mainly driven by early GPT-3.5 studies. Funnel plots showed no clear small-study effects, although interpretation was limited by the small number of studies. Risk of bias was predominantly high or moderate, largely due to non-systematic question selection and heterogeneous rating tools. Later models (GPT-4/4o and beyond) demonstrated higher performance compared with GPT-3.5. CONCLUSION: ChatGPT provides accurate, relevant, safe and clear responses to patient questions about HAS, while completeness shows moderate variability. Although LLMs appear promising as adjuncts to patient education, methodological limitations in the current evidence base underscore the need for expert clinical counselling and more rigorous, standardized evaluation frameworks. LEVEL OF EVIDENCE: Level III, systematic review and meta-analysis of non-randomized studies.

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Journal
Knee Surgery Sports Traumatology Arthroscopy
Published
2026-04-14
DOI
https://doi.org/10.1002/ksa.70396
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

ChatGPT provides accurate and safe responses to patient questions on hip arthroscopy, while completeness remains variable: A systematic review and single‐arm meta‐analysis

Maximilian Voss, Mikhail Salzmann, Nikolai Ramadanov, Maximilian Heinz et al.
Knee Surgery Sports Traumatology Arthroscopy
Artificial Intelligence in Healthcare and Education
article

ChatGPT provides accurate and safe responses to patient questions on hip arthroscopy, while completeness remains variable: A systematic review and single‐arm meta‐analysis

Maximilian Voss, Mikhail Salzmann, Nikolai Ramadanov, Maximilian Heinz, Пламен Пенчев, Timoty Osterberger, Roland Becker, Ingo J. Banke, Robert Prill
article en

Abstract

PURPOSE: Large language models, such as ChatGPT, are increasingly used by patients seeking information on hip arthroscopy (HAS) and femoroacetabular impingement (FAI). Despite their linguistic fluency, the accuracy, completeness and safety of procedure-specific patient information remain unclear. Although orthopaedic studies report variable performance across subspecialties, no systematic evaluation has specifically addressed HAS. METHODS: PubMed, Embase, Scopus, CINAHL and Epistemonikos were searched to 10 February 2026 for studies evaluating ChatGPT responses to patient-oriented HAS or FAI questions. Randomized and non-randomized studies, observational cohorts and case series were eligible. Data on question sources, model versions, rating systems and performance domains (accuracy, relevance, completeness, safety, readability and clarity) were extracted. Heterogeneous rating scales were dichotomized into high- versus low-quality responses. Risk of bias was assessed using QUADAS-2 and ROBINS-I. Random-effects single-arm meta-analyses (REML) were conducted for each domain. RESULTS: Eight studies met eligibility criteria. Accuracy was high (pooled 88.6%). Relevance, safety, readability and clarity reached pooled values of 100% with low heterogeneity. Completeness was lower (83.8%) with moderate heterogeneity, mainly driven by early GPT-3.5 studies. Funnel plots showed no clear small-study effects, although interpretation was limited by the small number of studies. Risk of bias was predominantly high or moderate, largely due to non-systematic question selection and heterogeneous rating tools. Later models (GPT-4/4o and beyond) demonstrated higher performance compared with GPT-3.5. CONCLUSION: ChatGPT provides accurate, relevant, safe and clear responses to patient questions about HAS, while completeness shows moderate variability. Although LLMs appear promising as adjuncts to patient education, methodological limitations in the current evidence base underscore the need for expert clinical counselling and more rigorous, standardized evaluation frameworks. LEVEL OF EVIDENCE: Level III, systematic review and meta-analysis of non-randomized studies.

Knee Surgery Sports Traumatology Arthroscopy
Medical University Plovdiv (BG), Walder Wyss (Switzerland) (CH), Medizinische Hochschule Brandenburg Theodor Fontane (DE), Universitätsklinikum Brandenburg an der Havel (DE), Technical University of Munich (DE)
Openalex Percentile: Top 18%
Artificial Intelligence in Healthcare and Education
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