Accuracy of the large language model ChatGPT in adult emergency department triage: a systematic review and meta-analysis
Abstract
AIM: This study employs a systematic review and meta-analysis to evaluate the accuracy and sensitivity (i.e., correct identification rate of high-risk patients at levels 1–2) of ChatGPT in adult emergency triage and to compare the potential of its different versions. METHODS: A systematic search of English and Chinese medical databases (2022–2025) was performed to identify studies directly comparing the triage performance of ChatGPT with that of human experts or a gold standard. The included studies comprised both simulated case scenarios and real-world clinical data from emergency departments. Literature screening adhered to the PICO framework. Methodological quality was evaluated using the QUADAS-2 tool. Data on overall accuracy and sensitivity were extracted. Meta-analysis was conducted in RevMan 5.4, with a predefined subgroup analysis based on the ChatGPT version. RESULTS: A total of 15 studies were included. The pooled accuracy was 0.51 (95% CI: 0.48–0.55) for ChatGPT-3.5, 0.70 (95% CI: 0.58–0.81) for ChatGPT-4 and its variants, and 0.81 for the optimized versions of ChatGPT (with the 95% confidence interval upper bound approaching 1.0 due to high heterogeneity). No significant differences were observed between ChatGPT and human triage in overall accuracy or sensitivity overall. Subgroup analyses showed that ChatGPT-3.5 performed similarly to humans on both metrics. In contrast, ChatGPT-4 and its variants demonstrated higher accuracy (RR = 1.04, P < 0.001) and…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.