ArticleJournal of experimental orthopaedics2024
Effectiveness of a large language model for clinical information retrieval regarding shoulder arthroplasty.
Article in Journal of experimental orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Effectiveness of a large language model for clinical information retrieval regarding shoulder arthroplasty.Journal of experimental orthopaedics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Purpose: To determine the scope and accuracy of medical information provided by ChatGPT-4 in response to clinical queries concerning total shoulder arthroplasty (TSA), and to compare these results to those of the Google search engine. Methods: A patient-replicated query for 'total shoulder replacement' was performed using both Google Web Search (the most frequently used search engine worldwide) and ChatGPT-4. The top 10 frequently asked questions (FAQs), answers, and associated sources were extracted. This search was performed again independently to identify the top 10 FAQs necessitating numerical responses such that the concordance of answers could be compared between Google and ChatGPT-4. The clinical relevance and accuracy of the provided information were graded by two blinded orthopaedic shoulder surgeons. Results: Concerning FAQs with numeric responses, 8 out of 10 (80%) had identical answers or substantial overlap between ChatGPT-4 and Google. Accuracy of information was not significantly different ( Conclusion: ChatGPT-4 provided trustworthy academic sources for medical information retrieval concerning TSA, while sources used by Google were heterogeneous. Accuracy and clinical relevance of information were not significantly different between ChatGPT-4 and Google. Level of Evidence: Level IV cross-sectional.
Indexed as
Identifiers
What OpenQuestion holds
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.