ReviewArchives of academic emergency medicine2024
Diagnostic Accuracy of ChatGPT for Patients' Triage; a Systematic Review and Meta-Analysis.
Review in Archives of academic emergency medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Accuracy of the large language model ChatGPT in adult emergency department triage: a systematic review and meta-analysis.BMC emergency medicine · 2026Pooled it
- Exploring an AI-driven dynamic triage system for real-time patient risk reassessment in emergency departments in low-resource settings.Frontiers in digital health · 2026Pooled it
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.JMIR cancer · 2026Article
- Large language models for post-discharge follow-up in erectile dysfunction care: a narrative review.Translational andrology and urology · 2026Review
- Generative Artificial Intelligence as a First Responder in Adolescent Testicular Torsion: A Case Report.Cureus · 2026Article
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- Multi-Evidence Clinical Reasoning With Retrieval-Augmented Generation for Emergency Triage: Retrospective Evaluation Study.JMIR medical informatics · 2026Article
- Performance of successive generative pretrained transformers (GPT) models in medical cases and board style questions.Scientific reports · 2026Article
- Evaluating ChatGPT's Triage and Diagnostic Capabilities in Patients Presenting With Common Causes of Foot and Ankle Pain.Foot & ankle orthopaedics · 2026Article
- Nurse-led digital triage and health literacy navigation: redefining the front door of public health in the artificial intelligence era.Frontiers in public health · 2026Article
- Article
- AI powered patient records analysis for injury surveillance in children and adolescents - a feasibility study from Switzerland.International journal of public health · 2026Article
- Should we leave paediatric emergency triage to artificial intelligence? A comparison of ChatGPT 4o and Grok 3.Frontiers in pediatrics · 2026Article
- Evaluating GPT-4o for emergency disposition of complex respiratory cases with pulmonology consultation: a diagnostic accuracy study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Observational
- Piloting Temperature-Driven Variability in Emergency Diagnostic Accuracy Using a Leading Large Language Model.Cureus · 2025Article
- Artificial Intelligence and Advanced Digital Health for Hypertension: Evolving Tools for Precision Cardiovascular Care.Medicina (Kaunas, Lithuania) · 2025Review
- Explainable illicit drug abuse prediction using hematological differences.Scientific reports · 2025Article
- Comparison of triage performance among DRP tool, ChatGPT, and outpatient rehabilitation doctors.Scientific reports · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
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. Conclusion: 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.
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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.