Evidence map›Paper›PMID 41451615›Full record

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

ChatGPT models provide higher-quality but lower-readability responses than Google Gemini regarding anterior shoulder instability, with no added benefit of the orthopaedic expert plugin.

Khaled Skaik, Sean Omoseni, Danielle Dagher, Darshil Shah, Theodorakys Marín Fermín, Piero Agostinone, Ashraf Hantouly, Moin Khan

Abstract readComparative Study
In one paragraph

Article 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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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. Article
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

8 authors.

Khaled SkaikFaculty of Medicine & Health Sciences, McGill University, Montreal, Quebec, Canada.ORCID https://orcid.org/0000-0002-0480-8440
Sean OmoseniSchool of Medicine, University of Edinburgh, Edinburgh, Scotland, UK.
Danielle DagherDivision of Orthopaedic Surgery, McMaster University, Hamilton, Ontario, Canada.ORCID https://orcid.org/0000-0002-0372-8991
Darshil ShahDivision of Orthopaedic Surgery, McMaster University, Hamilton, Ontario, Canada.
Theodorakys Marín FermínSanta Sofía Hospital, Caracas, Venezuela.ORCID https://orcid.org/0000-0002-1698-9517
Piero AgostinoneBiomedical and Neuromotor Sciences Department, University of Bologna, Bologna, Italy.
Ashraf HantoulyDivision of Orthopaedic Surgery, McMaster University, Hamilton, Ontario, Canada.
Moin KhanDivision of Orthopaedic Surgery, McMaster University, Hamilton, Ontario, Canada.ORCID https://orcid.org/0000-0002-8237-8095

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose is to analyze and compare the quality and readability of information regarding anterior shoulder instability and shoulder stabilization surgery from three LLMs: ChatGPT 4o, ChatGPT Orthopaedic Expert (OE) and Google Gemini.

methodsChatGPT 4o, ChatGPT OE and Google Gemini were used to answer 21 commonly asked questions from patients on anterior shoulder instability. The responses were independently rated by three fellowship-trained orthopaedic surgeons using the validated Quality Analysis of Medical Artificial Intelligence (QAMAI) tool. Assessors were blinded to the model, and evaluations were performed twice, 3 weeks apart. Readability was measured using Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL). This study adhered to TRIPOD-LLM. Statistical analysis included the Friedman test, the Wilcoxon signed-rank tests and inter-class coefficients.

resultsInter-rater reliability between three surgeons was good or excellent reliability in all LLMs. ChatGPT OE and ChatGPT 4o demonstrated comparable overall performance, each achieving a median QAMAI score of 22 with interquartile ranges (IQRs) of 5.25 and 6.75, respectively, with median (IQR) domain scores for accuracy 4 (1) and 4 (1), clarity 4 (1) and 4 (1), relevance 4 (1) and 4 (1), completeness 4 (1) and 4 (1), provision of sources 1 (0) for both and usefulness 4 (1) and 4 (1), respectively. Google Gemini showed lower scores across these domains (accuracy 3 [1], clarity 3 [1], relevance 3 [1.25], completeness 3 [0.25], sources 3 [3] and usefulness 3 [1.25]), with a median QAMAI score of 19 (5.25) (p < 0.01 vs. each ChatGPT model). Readability was higher for Google Gemini (FRES = 36.96, FKGL = 11.92) than for ChatGPT OE (FRES = 21.90, FKGL = 14.94) and ChatGPT 4o (FRES = 24.24, FKGL = 15.11), indicating easier-to-read content (p < 0.01). There was no significant difference between ChatGPT 4o and OE in overall quality or readability.

conclusionsChatGPT 4o and ChatGPT OE provided statistically higher-quality responses than Google Gemini, though all models showed good-quality responses overall. However, responses generated by ChatGPT 4o and OE were more difficult to read than those generated by Google Gemini. LEVEL OF EVIDENCE: Level V, expert opinion.

Indexed as

ComprehensionJoint InstabilityShoulder JointGenerative Artificial IntelligenceHumansOrthopedicsReproducibility of Resultsanterior shoulder instabilityartificial intelligenceChatGPTlarge language modelsshoulder stabilization surgery

Identifiers

PMID41451615
PMCPMC12850548

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.