ArticleFrontiers in public health2026
Performance evaluation of large language models in the diagnosis of emergency internal medicine diseases: a retrospective study.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- [Application effect of LLMs as decision-support tools in emergency burn treatment].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2026Article
- Problematic generative AI use and mental health risks among Chinese university students: latent profiles, network structure, and health literacy as a modifiable resource.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Medical domain large language models (LLMs) exhibit verified clinical decision-support capabilities in simulated case analyses and standardized tests, yet their diagnostic efficacy in real-world emergency settings remain insufficiently explored. This study evaluates the diagnostic performance of 5 mainstream LLMs (ChatGPT-4o, Gemini-2.0, Grok3, DeepSeek-V3, Doubao) against emergency department junior physicians (EDJP) on real-world emergency internal medicine cases. Methods: A single-center retrospective analysis design was conducted. 154 anonymized emergency internal medicine patients of the Second Affiliated Hospital of Zhejiang University School of Medicine from January to May 2025 were included, covering common acute diseases of multiple systems. 15 EDJPs and 5 LLMs were selected to diagnose the cases, respectively. The main diagnostic accuracy, comprehensiveness of differential diagnosis, and response time were used as evaluation indicators. Non-parametric tests were used for statistical analysis. Results: (1) Main diagnostic accuracy: DeepSeek-V3 (90.0%), ChatGPT-4o (86.0%), and Grok3 (86.0%) were significantly higher than that of EDJP (77.5%, Conclusion: This retrospective study shows that LLMs outperformed EDJPs in diagnostic accuracy, differential diagnosis comprehensiveness and response efficiency for emergency internal medicine diseases, demonstrating significant potential for clinical decision support. Subsequent efforts will focus on exploring how to effectively integrate into physician-led collaborative workflows to enhance emergency care quality and efficiency.
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.