Evidence map›Paper›PMID 40486807›Full record

ArticleJSES international2025

Adequacy of ChatGPT responses to frequently asked questions about shoulder arthroplasty: is it an appropriate adjunct for patient education?

Christopher K Johnson, Krishna Mandalia, Jason Corban, Kaley E Beall, Sarav S Shah

Abstract read
In one paragraph

Article in JSES international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

5 authors.

Christopher K JohnsonDepartment of Orthopedic Surgery, New England Baptist Hospital, Boston, MA, USA.
Krishna MandaliaTufts University School of Medicine, Boston, MA, USA.
Jason CorbanDepartment of Orthopedic Surgery, New England Baptist Hospital, Boston, MA, USA.
Kaley E BeallDepartment of Orthopedic Surgery, New England Baptist Hospital, Boston, MA, USA.
Sarav S ShahDepartment of Orthopedic Surgery, New England Baptist Hospital, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) large language models, such as ChatGPT, have numerous novel applications in medicine, one of which is patient education. Several studies in other specialties have investigated the adequacy of ChatGPT-generated responses to frequently asked questions (FAQs) by patients, with largely positive results. The purpose of this study is to evaluate the accuracy and clarity of ChatGPT-generated responses to website-derived FAQs relating to shoulder arthroplasty. Methods: Ten questions regarding shoulder arthroplasty were compiled from the websites of 5 leading academic institutions. These questions were rated on a scale from 1 to 4, corresponding to "excellent response not requiring clarification," "satisfactory requiring minimal clarification," "satisfactory requiring moderate clarification," and "unsatisfactory requiring substantial clarification," respectively, by 2 orthopedic surgeons. A senior shoulder arthroplasty surgeon arbitrated disagreements. Cohen's Kappa coefficient was utilized to assess inter-rater agreement. Results: After arbitration, only one response was rated as "excellent response not requiring clarification." Nine of 10 responses required clarification. Four were rated as a "satisfactory requiring minimal clarification," 5 were rated as a "satisfactory requiring moderate clarification," and none were rated as "unsatisfactory requiring substantial clarification". The Kappa coefficient was 0.516 ( Conclusion: When queried with FAQs regarding shoulder arthroplasty, ChatGPT's responses were all deemed 'satisfactory', but most required clarification. This may be due to the nuances of anatomic vs. reverse shoulder replacement. Thus, patients may find benefit in using ChatGPT to guide whether or not they should seek medical attention, but are limited in the detail and accuracy of treatment-related questions. While a helpful tool to start provider-patient conversations, it does not appear that ChatGPT provides quality, verified, data-driven answers at this time, and should be used cautiously in conjunction to provider-patient discussions. Although the use of ChatGPT in answering FAQs is limited at the moment, orthopedic surgeons should continue to monitor the use of ChatGPT as a patient education tool, as well as the expanding use of AI as a possible adjunct in clinical decision-making.

Indexed as

Artificial intelligence (AI)ChatGPTPatient communicationPerioperative questionsShoulder arthroplastySports medicine

Identifiers

PMID40486807
PMCPMC12145054

What Socratic holds

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LicenceCC BY-NC-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 Socratic graph.