NeurologyNCT06779292
Application of Large Language Models in Emergency Neurology
An observational study of Neurology and Emergency, run by Capital Medical University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- What is being tested?
- Large Language Model Diagnosis
- How many people
- 433 participants
- Where
- 1 siteChina
- Study dates
- 1 Feb 2025to 7 Apr 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients in the emergency neurology department
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Xuanwu Hospital, Capital Medical University
Beijing, Beijing Municipality, 100053
About this study
Official title: Application of Multimodal Large Language Models in Emergency Neurology Diagnosis
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.
- Being tested
- Large Language Model Diagnosis
- Medical subject headings
- Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Large Language Model DiagnosisDiagnostic test
Using the large language model for diagnosing emergency neurology conditions.
| Study group | Role | What they receive |
|---|---|---|
| Patients presenting to the emergency neurology department. | Not described Receives: Diagnostic Test: Large Language Model Diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 month
Secondary outcomes (4)
Feasibility of treatment plans
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.
Measured: 1 month
dignostic specificity
A comparison of dianostic specificity between large language model diagnosis and emergency department physicians diagnosis
Measured: 1 month
Diagnostic Sensitivity
A comparison of dianostic sensitivity between large language model diagnosis and emergency department physicians diagnosis.
Measured: 1 month
False Discovery Rate
A comparison of the false discovery rate between large language model diagnosis and emergency department physicians diagnosis.
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jan 2025
- Study start1 Feb 2025
- Primary completion7 Apr 2025
- Study completion7 Apr 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for NeurologyStudies of the same condition, open and under way.
- More trials run by Capital Medical UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 433 participants (actual)
Sponsor and investigators
- Lead sponsor
- Capital Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ji Xunming,MD,PhD, Capital Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06779292
- Sponsor's protocol ID
- ALEGN
- Status last verified
- Apr 2025
- First submitted
- 6 Jan 2025
- Last updated
- 15 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Guo Y, Meng X, Yu E, Zhang W, Yang Y, Ma H, Shao C, Wang W, Wang R, Wang H, Meng R, Zhao W, Song Z, Ji X, Wu C. Development and prospective shadow evaluation of a domain-specific large language model for emergency neurological diagnosis. NPJ Digit Med. 2026 Apr 18;9(1):470. doi: 10.1038/s41746-026-02644-z.
PubMed 42000879
Study at a glance
From the registry record, last updated by the sponsor on 15 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Multimodal Large Language Models in Emergency Neurology Diagnosis
- ClinicalTrials.gov ID
- NCT06779292
- Acronym / sponsor ID
- ALEGN
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 433 participants (actual)
- Lead sponsor
- Capital Medical UniversityUniversity, hospital or other
- Study start
- 1 Feb 2025 (actual)
- Primary completion
- 7 Apr 2025 (actual)
- Study completion
- 7 Apr 2025 (actual)
- First posted
- 16 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients presenting to the emergency neurology department. | – | Diagnostic Test: Large Language Model Diagnosis |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients in the emergency neurology department
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Large Language Model DiagnosisUsing the large language model for diagnosing emergency neurology conditions. | Diagnostic test | Patients presenting to the emergency neurology department. |
Primary outcome
dignostic accuracy
Time frame: 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.
4 secondary outcomes
Feasibility of treatment plans
Time frame: 1 month
dignostic specificity
Time frame: 1 month
Diagnostic Sensitivity
Time frame: 1 month
False Discovery Rate
Time frame: 1 month
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Apr 2025.
- Derived from this study: PubMed 42000879Guo Y, Meng X, Yu E, Zhang W, Yang Y, Ma H, Shao C, Wang W, Wang R, Wang H, Meng R, Zhao W, Song Z, Ji X, Wu C. Development and prospective shadow evaluation of a domain-specific large language model for emergency neurological diagnosis. NPJ Digit Med. 2026 Apr 18;9(1):470. doi: 10.1038/s41746-026-02644-z.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neurology and Emergency, run by Capital Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
dignostic accuracy (time frame: 1 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06779292, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Apr 2025.