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Application of Large Language Models in Emergency Neurology

NCT06779292 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 433

Last updated 2025-04-15

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

Large Language Model Diagnosis

Using the large language model for diagnosing emergency neurology conditions.

Sponsors & Collaborators

  • Capital Medical University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-02-01
Primary Completion
2025-04-07
Completion
2025-04-07

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06779292 on ClinicalTrials.gov