Effectiveness of a large language model for clinical information retrieval regarding shoulder arthroplasty

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: = 0.0025). 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.

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