Application of Large Language Models in Emergency Neurology

Completed

• Conditions: Neurology; Emergency

• Registration Number: NCT06779292
• Lead Sponsor: Capital Medical University

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2025-01-06
• Study First Submitted QC: 2025-01-15
• Study First Posted: 2025-01-16
• Study Start: 2025-02-01
• Status Verified: 2025-04
• Primary Completion: 2025-04-07
• Study Completion: 2025-04-07
• Last Update Submitted: 2025-04-14
• Last Update Posted: 2025-04-15

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 433

Inclusion Criteria

• 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.

Exclusion Criteria

• 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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
dignostic accuracy	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.

Secondary Outcome Measures

Name	Time	Method
Feasibility of treatment plans	1 month	Experts use the Emergency Treatment Recommendation Scoring Scale to evaluate the treatment suggestions from conventional methods and large language models. The maximum score is 5 and the minimum score is 1, with 5 representing strong agreement with the recommendation.
dignostic specificity	1 month	A comparison of dianostic specificity between large language model diagnosis and emergency department physicians diagnosis
Diagnostic Sensitivity	1 month	A comparison of dianostic sensitivity between large language model diagnosis and emergency department physicians diagnosis.
False Discovery Rate	1 month	A comparison of the false discovery rate between large language model diagnosis and emergency department physicians diagnosis.

Trial Locations

Locations (1)

Xuanwu Hospital, Capital Medical University; 🇨🇳 Beijing, Beijing, China