Domain-Specific Large Language Model Assistance for Emergency Neurological Diagnosis and Treatment（DEMAND）
Domain-Specific Large Language Model Assistance for Emergency Neurological Diagnosis and Treatment: A Randomized Controlled Clinical Trial
Capital Medical University
1,360 participants
Jun 9, 2026
INTERVENTIONAL
Conditions
Summary
This study will evaluate whether Xuanwu-NeuroAid 2.0, a large language model for emergency neurology, can improve 30-day diagnostic quality in adults with acute neurological symptoms. Physicians will be randomly assigned to AI-assisted care or usual care. In the AI-assisted group, the model will provide diagnostic and management suggestions, while physicians will make all final clinical decisions. The usual-care group will receive standard emergency neurology care without large language model assistance.
Eligibility
Inclusion Criteria3
- Age ≥18 years;
- Presentation to the emergency neurology service with acute neurological symptoms;
- Written informed consent provided by the patient or a legally authorized representative.
Exclusion Criteria6
- Presentation primarily for trauma;
- Pregnancy;
- Requiring immediate life-saving interventions;
- Estimated life expectancy of less than 30 days;
- Participation in another clinical trial within the previous 30 days or in a trial that could interfere with the study or outcome assessment;
- Any condition that, in the opinion of the investigators, would interfere with the conduct of the trial or the interpretation of the results.
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Interventions
Xuanwu-NeuroAid 2.0 is a large language model used to support emergency neurology evaluation and management. It generates diagnostic and management suggestions based on available clinical information, including history, physical examination, laboratory results, and imaging data. Physicians may interact with the model multiple times, but remain responsible for all final clinical decisions. Its recommendations may be overridden when considered inappropriate.
Locations(3)
View Full Details on ClinicalTrials.gov
For the most up-to-date information, visit the official listing.
NCT07626229