Evidence map›Paper›PMID 40080671›Full record

ArticleJournal of the American Academy of Orthopaedic Surgeons. Global research & reviews2025

Evaluating if ChatGPT Can Answer Common Patient Questions Compared With OrthoInfo Regarding Rotator Cuff Tears.

Alexander Jurayj, Julio Nerys-Figueroa, Emil Espinal, Michael A Gaudiani, Travis Baes, Jared Mahylis, Stephanie Muh

Abstract readComparative Study
In one paragraph

Article in Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Letter to the Editor: ChatGPT Can and Common Patient Questions Compared With OrthoInfo Regarding Rotator Cuff Tears: Comment.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
    Article
  2. Article
  3. Article
  4. 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

7 authors.

Alexander JurayjFrom the Department of Orthopaedic Surgery, Henry Ford Hospital, Detroit, MI.
Julio Nerys-Figueroa
Emil Espinal
Michael A Gaudiani
Travis Baes
Jared Mahylis
Stephanie Muh

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate ChatGPT's (OpenAI) ability to provide accurate, appropriate, and readable responses to common patient questions about rotator cuff tears.

methodsEight questions from the OrthoInfo rotator cuff tear web page were input into ChatGPT at two levels: standard and at a sixth-grade reading level. Five orthopaedic surgeons assessed the accuracy and appropriateness of responses using a Likert scale, and the Flesch-Kincaid Grade Level measured readability. Results were analyzed with a paired Student t-test.

resultsStandard ChatGPT responses scored higher in accuracy (4.7 ± 0.47 vs. 3.6 ± 0.76; P < 0.001) and appropriateness (4.5 ± 0.57 vs. 3.7 ± 0.98; P < 0.001) compared with sixth-grade responses. However, standard ChatGPT responses were less accurate (4.7 ± 0.47 vs. 5.0 ± 0.0; P = 0.004) and appropriate (4.5 ± 0.57 vs. 5.0 ± 0.0; P = 0.016) when compared with OrthoInfo responses. OrthoInfo responses were also notably better than sixth-grade responses in both accuracy and appropriateness (P < 0.001). Standard responses had a higher Flesch-Kincaid grade level compared with both OrthoInfo and sixth-grade responses (P < 0.001).

conclusionStandard ChatGPT responses were less accurate and appropriate, with worse readability compared with OrthoInfo responses. Despite being easier to read, sixth-grade level ChatGPT responses compromised on accuracy and appropriateness. At this time, ChatGPT is not recommended as a standalone source for patient information on rotator cuff tears but may supplement information provided by orthopaedic surgeons.

Indexed as

Patient Education as TopicRotator Cuff InjuriesComprehensionGenerative Artificial IntelligenceHumansSurveys and Questionnaires

Identifiers

PMID40080671
PMCPMC11905972

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

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.