ArticleJSES international2025
Evaluation of ChatGPT's responses to frequently asked questions about shoulder arthroplasty.
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 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients now have access to numerous resources about common orthopedic procedures before clinical consultations. Recently, artificial intelligence (AI)-powered chatbots have gained popularity, offering human-like, convincing responses. A prominent example is ChatGPT, an AI-based chat technology, which has seen rapid growth in acceptance. Given the increasing likelihood that patients will turn to this technology for educational information prior to undergoing surgical intervention, we aimed to determine ChatGPT's ability to provide adequate answers to frequently asked questions about shoulder arthroplasty. Methods: After a literature review, we formulated the 10 most frequently asked questions about shoulder arthroplasty and presented them to the chatbot without follow-up questions or repetitions. The responses were analyzed by 15 independent shoulder surgeons, who evaluated each response for accuracy using an evidence-based approach. The responses were classified as "excellent response without need for clarification," "satisfactory requiring minimal clarification," "satisfactory requiring moderate clarification," or "unsatisfactory requiring substantial clarification." Results: Of the responses given by the chatbot, 45.5% of the time the expert panel rated the response as "excellent," 38.2% of the time the rating was "satisfactory requiring minimal clarification," and only 11.8% and 4.5% of the responses were rated as "satisfactory requiring moderate clarification" and "unsatisfactory," respectively. Conclusion: ChatGPT provides reliable information to patients undergoing shoulder arthroplasty. As a complementary educational resource, AI shows potential by delivering accurate, evidence-based answers with little need for clarification and minimal bias. Despite limitations, the study underscores the promising role of AI-based platforms in healthcare.
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Registered trials
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.