ReviewCureus2025
A Practical Guide to the Utilization of ChatGPT in the Emergency Department: A Systematic Review of Current Applications, Future Directions, and Limitations.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis 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
6 citing papers in PubMed, 1 synthesis or guideline 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
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
- Flexing ChatGPT-4o's Diagnostic Muscle: Detection of Fractures in the Ossifying Pediatric Elbow on Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- ChatGPT's performance in sample size estimation: a preliminary study on the capabilities of artificial intelligence.Family practice · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of artificial intelligence (AI) tools across various medical specialties highlights the potential for AI to transform medicine over the next 20 years. Despite this potential, the adoption of AI can feel incremental and disconnected from the daily practice of individual clinicians. For emergency department (ED) physicians practicing in 2025, recognizing and evaluating AI tools available for immediate integration into practice is essential. One such tool is ChatGPT (OpenAI, San Francisco, California, United States), a large language model (LLM) that is free, easily accessible via smartphones or computers, and widely used across industries. However, its usability in the ED setting remains poorly characterized. This review explores the current evidence surrounding ChatGPT 4's applications in various ED physician tasks, documenting its strengths and limitations. While ChatGPT demonstrates significant utility in language generation and administrative tasks, its potential for supporting more complex tasks in medical decision-making is emerging but not yet robust. The available evidence is limited and variable and lacks standardization, reflecting a field still in its early stages of development. Notably, the performance improvements observed between ChatGPT 3.5 and ChatGPT 4 suggest that future iterations, such as the anticipated release of ChatGPT 5, could significantly impact these findings. This review provides a comprehensive snapshot of the current state of evidence regarding ChatGPT's use in the ED, offering both an evaluation of its capabilities and a practical guide for its appropriate use by ED clinicians today.
Indexed as
Identifiers
What OpenQuestion holds
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.