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Diagnostic Accuracy of Chatgpt for Patients’ Triage; a Systematic Review and Meta-Analysis Publisher



N Kaboudi NAVID ; S Firouzbakht SAEEDEH ; Ms Eftekhar Mohammad SHAHIR ; F Fayazbakhsh FATEMEH ; N Joharivarnoosfaderani NILOUFAR ; S Ghaderi SALAR ; M Dehdashti MOHAMMADREZA ; Ym Kia Yasmin MOHTASHAM ; M Afshari MARYAM ; M Vasaghigharamaleki MARYAM
Authors

Source: Archives of Academic Emergency Medicine Published:2024


Abstract

Introduction: Artificial intelligence (AI), particularly ChatGPT developed by OpenAI, has shown the potential to improve diagnostic accuracy and efficiency in emergency department (ED) triage. This study aims to evaluate the diagnostic performance and safety of ChatGPT in prioritizing patients based on urgency in ED settings. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive literature searches were performed in Scopus, Web of Science, PubMed, and Embase. Studies evaluating ChatGPT’s diagnostic performance in ED triage were included. Quality assessment was conducted using the QUADAS-2 tool. Pooled accuracy estimates were calculated using a random-effects model, and heterogeneity was assessed with the I2 statistic. Results: Fourteen studies with a total of 1, 412 patients or scenarios were included. ChatGPT 4.0 demonstrated a pooled accuracy of 0.86 (95% CI: 0.64–0.98) with substantial heterogeneity (I2 = 93%). ChatGPT 3.5 showed a pooled accuracy of 0.63 (95% CI: 0.43–0.81) with significant heterogeneity (I2 = 84%). Funnel plots indicated potential publication bias, particularly for ChatGPT 3.5. Quality assessments revealed varying levels of risk of bias and applicability concerns. Conclusions: ChatGPT, especially version 4.0, shows promise in improving ED triage accuracy. However, significant variability and potential biases highlight the need for further evaluation and enhancement. © 2024 Elsevier B.V., All rights reserved.
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