Application of Large Language Models in Emergency Neurology

NCT06779292 | Completed

• 1 other identifier: ALEGN
• Study Type: observational
• Target: 433
• Locations: 1 country
• Sites: 1

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.

Conditions

Neurology; Emergency

Outcome Measures

Primary Outcomes (1)

• dignostic accuracy

  To evaluate the consistency between the diagnosis made by large language models for emergency patients and the confirmed diagnosis after inpatient or outpatient visits.

  1 month

Secondary Outcomes (4)

• Feasibility of treatment plans

  1 month

• dignostic specificity

  1 month

• Diagnostic Sensitivity

  1 month

• False Discovery Rate

  1 month

Study Arms (1)

Patients presenting to the emergency neurology department.

Diagnostic Test: Large Language Model Diagnosis

Interventions

Large Language Model Diagnosis DIAGNOSTIC_TEST

Using the large language model for diagnosing emergency neurology conditions.

Patients presenting to the emergency neurology department.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients in the emergency neurology department

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Capital Medical University: lead

Study Sites (1)

Xuanwu Hospital, Capital Medical University

Beijing, Beijing Municipality, 100053, China

Location

MeSH Terms

Conditions

Emergencies

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor, Beijing Institute of Brain Disorders, Capital Medical University

Study Record Dates

• First Submitted: January 6, 2025
• First Posted: January 16, 2025
• Study Start: February 1, 2025
• Primary Completion: April 7, 2025
• Study Completion: April 7, 2025
• Last Updated: April 15, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)