Application of Multimodal Large Language Models in Emergency Neurology Diagnosis
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 433
- 试验地点
- 1
- 主要终点
- dignostic accuracy
研究概览
简要总结
Emergency neurology covers a wide range of conditions, often involving urgent situations such as acute cerebrovascular diseases, seizures, central nervous system infections, and consciousness disorders. However, due to the time constraints in emergency care and limited patient information collection, misdiagnosis and missed diagnoses are common issues. Large language models (LLMs) possess powerful natural language processing and knowledge reasoning capabilities, enabling them to directly handle and understand complex, unstructured medical data such as patient medical records, dialogue notes, and laboratory test results. LLMs show broad potential for application in complex medical scenarios. This study aims to evaluate the application value of LLMs in emergency neurology, specifically examining their diagnostic accuracy in emergency neurology conditions, analyzing the feasibility of treatment plans and further examination recommendations proposed by the model, and exploring their potential in improving diagnostic efficiency and aiding decision-making.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ≥18-80 years, male or female.
- •Patients seeking emergency neurology care.
- •Patients who can provide complete medical records (including consultation recordings, physical examination, test results, etc.).
- •Voluntary participation and signing of informed consent.
排除标准
- •Patients who directly enter the resuscitation process due to the severity of their condition(e.g., patients who are immediately placed in the ICU).
- •Patients with unstable vital signs.
- •Patients who are unable to communicate effectively (e.g., severe consciousness impairment or severe cognitive disorders).
- •Patients who are currently participating in other clinical trials.
结局指标
主要结局
dignostic accuracy
时间窗: 1 month
To evaluate the consistency between the diagnosis made by large language models for emergency patients and the confirmed diagnosis after inpatient or outpatient visits.
次要结局
- Feasibility of treatment plans(1 month)
- dignostic specificity(1 month)
- Diagnostic Sensitivity(1 month)
- False Discovery Rate(1 month)
研究者
Ji Xunming,MD,PhD
Professor, Beijing Institute of Brain Disorders, Capital Medical University
Capital Medical University