ArticleNPJ digital medicine2026
Development and prospective shadow evaluation of a domain-specific large language model for emergency neurological diagnosis.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06779292 (Application of Multimodal Large Language Models in Emergency Neurology Diagnosis), which is not on this map. Cited by 1 paper.
What it found
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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.
Application of Multimodal Large Language Models in Emergency Neurology Diagnosis
Who cites it
1 citing paper 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Large language models (LLMs) show promise in emergency medicine; however, their role in emergency neurology remains unclear. We developed a customized LLM, Xuanwu-NeuroAid, and prospectively enrolled 433 patients. The diagnostic outputs of the model and emergency physicians were compared with confirmed diagnoses, and an expert panel performed blinded evaluations of the recommendations generated by both the model and physicians. The independent diagnostic accuracy of the model was 79.4%, which was significantly higher than that of emergency physicians (65.4%, p < 0.001). Blinded expert assessments indicated that the model's recommendations for examinations and treatments were significantly more comprehensive, accurate, and clinically applicable than those of physicians (p < 0.001). Moreover, the inclusion of demographic information altered the model's recommendations for health education, suggesting a sensitivity to sociodemographic factors. Our findings highlight the potential of the LLM to enhance diagnostic precision and support decision-making in emergency neurology under simulated clinical conditions. This study was registered at ClinicalTrials.gov (NCT06779292; January 6, 2025).
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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.