Enhancing Musculoskeletal Injection Safety: Evaluating Checklists Generated by Artificial Intelligence and Revising the Preformed Checklist.

Background Musculoskeletal disorders are a significant global health issue, necessitating advanced management strategies such as intra-articular and extra-articular injections to alleviate pain, inflammation, and mobility challenges. As the adoption of these interventions by physicians grows, the importance of robust safety protocols becomes paramount. This study evaluates the effectiveness of conversational artificial intelligence (AI), particularly versions 3.5 and 4 of Chat Generative Pre-trained Transformer (ChatGPT), in creating patient safety checklists for managing musculoskeletal injections to enhance the preparation of safety documentation. Methodology A quantitative analysis was conducted to evaluate AI-generated safety checklists against a preformed checklist adapted from reputable medical sources. Adherence of the generated checklists to the preformed checklist was calculated and classified. The Wilcoxon signed-rank test was used to assess the performance differences between ChatGPT versions 3.5 and 4. Results ChatGPT-4 showed superior adherence to the preformed checklist compared to ChatGPT-3.5, with both versions classified as very good in safety protocol creation. Although no significant differences were present in the sign-in and sign-out parts of the checklists of both versions, ChatGPT-4 had significantly higher scores in the procedure planning part (p = 0.007), and its overall performance was also higher (p < 0.001). Subsequently, the preformed checklist was revised to incorporate new contributions from ChatGPT. Conclusions ChatGPT, especially version 4, proved effective in generating patient safety checklists for musculoskeletal injections, highlighting the potential of AI to streamline clinical practices. Further enhancements are necessary to fully meet the medical standards.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright © 2024, Yilmaz Muluk et al.)