Evidence map›Paper›PMID 41979367›Full record

SynthesisKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2026

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

Nikolai Ramadanov, Plamen Penchev, Maximilian Voss, Maximilian Heinz, Robert Prill, Mikhail Salzmann, Roland Becker, Timoty Osterberger, Ingo J Banke

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Nikolai RamadanovCenter of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany.ORCID https://orcid.org/0000-0003-4669-8187
Plamen PenchevFaculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria.
Maximilian VossCenter of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany.
Maximilian HeinzCenter of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany.
Robert PrillCenter of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany.
Mikhail SalzmannCenter of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany.
Roland BeckerCenter of Orthopaedics and Traumatology, Brandenburg Medical School, University Hospital Brandenburg an der Havel, Brandenburg an der Havel, Germany.
Timoty OsterbergerClinic of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.
Ingo J BankeClinic of Orthopaedics and Sports Orthopaedics, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeLarge 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.

methodsPubMed, 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.

resultsEight 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.

conclusionChatGPT 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.

Indexed as

ArthroscopyFemoracetabular ImpingementHip JointPatient Education as TopicGenerative Artificial IntelligenceHumansLarge Language ModelsaccuracyChatGPTcompletenessfemoroacetabular impingementhip arthroscopymeta‐analysis

Identifiers

PMID41979367
PMCPMC13266954

What OpenQuestion holds

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Registered trials

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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.