Emergency, Neurology
Conditions
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.
Interventions
DIAGNOSTIC_TESTLarge Language Model Diagnosis
Using the large language model for diagnosing emergency neurology conditions.
Sponsors
Capital Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Outcome results
None listed