Ignite Creation Date:
2026-03-26 @ 3:15 PM
Ignite Modification Date:
2026-03-31 @ 3:28 AM
Study NCT ID:
NCT07344792
Status:
COMPLETED
Last Update Posted:
2026-01-29
First Post:
2025-12-01
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Physician Diagnosis of Neurologic Cases With Large Language Models
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 40}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2026-01-17', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2026-01-28', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2026-01-28', 'studyFirstSubmitDate': '2025-12-01', 'studyFirstSubmitQcDate': '2026-01-08', 'lastUpdatePostDateStruct': {'date': '2026-01-29', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-01-15', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-01-28', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Top-1 diagnostic accuracy', 'timeFrame': 'Baseline', 'description': "Top-1 diagnosis is considered correct if the physician's final top-ranked diagnosis matches the reference diagnosis or a clinically similar diagnosis judged as equivalent by neurologists."}], 'secondaryOutcomes': [{'measure': 'Top-3 diagnostic accuracy', 'timeFrame': 'Baseline', 'description': "Top-3 diagnosis is considered correct if the reference diagnosis or a clinically similar diagnosis judged as equivalent by neurologists appears among the physician's top three final diagnoses."}, {'measure': 'Time spent on diagnosis', 'timeFrame': 'Baseline', 'description': 'Time participants spend per case.'}, {'measure': 'Diagnostic confidence', 'timeFrame': 'Baseline', 'description': "Physicians' diagnostic confidence will be measured using a five-point Likert scale (1=very unsure, 5=very sure)."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Neurology']}, 'descriptionModule': {'briefSummary': "This study is a prospective randomized controlled trial that aims to evaluate whether large language model (LLM) assistance improves physicians' diagnostic performance compared with conventional assistance.", 'detailedDescription': "The study population consists of licensed physicians who have completed at least two years of neurology training. Participants are randomly assigned in a 1:1 ratio to the LLM assistance group or the conventional assistance group. In the LLM assistance group, physicians are assisted by an in-house LLM and are allowed to use conventional resources (e.g., search engines, UpToDate, or clinical guidelines) before submitting a final diagnosis. Access to any other LLMs is not permitted. In the conventional assistance group, physicians use only conventional resources and do not have access to the in-house LLM or any other LLMs at any point during the study. The primary outcome is top-1 diagnostic accuracy. Secondary outcomes include top-3 diagnostic accuracy, the time required to complete each case, and physicians' diagnostic confidence."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Licensed physicians who have completed at least two years of neurology training.\n* Willing to participate in the study and able to provide written informed consent.\n\nExclusion Criteria:\n\n* Physicians who have previously participated in the development of the in-house LLM or the construction of datasets used in this study.\n* Physicians who were involved in the clinical care of patients whose cases were used to construct the case dataset for this study.\n* Physicians who are known to have previously reviewed or been exposed to the specific cases used in this study.'}, 'identificationModule': {'nctId': 'NCT07344792', 'briefTitle': 'Physician Diagnosis of Neurologic Cases With Large Language Models', 'organization': {'class': 'OTHER', 'fullName': 'Capital Medical University'}, 'officialTitle': 'Physician Diagnosis of Neurologic Cases Assisted by Large Language Models: A Randomized Controlled Trial', 'orgStudyIdInfo': {'id': 'LLM-001'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'LLM assistance group', 'description': 'Physicians are assisted by an in-house LLM and are allowed to use conventional resources. Access to any other LLMs is not permitted.', 'interventionNames': ['Other: LLM assistance']}, {'type': 'NO_INTERVENTION', 'label': 'Conventional assistance group', 'description': 'Physicians use only conventional resources and do not have access to the in-house LLM or any other LLMs at any point during the study.'}], 'interventions': [{'name': 'LLM assistance', 'type': 'OTHER', 'description': 'Physicians are assisted by an in-house large language model and are allowed to use conventional resources. Access to any other LLMs is not permitted.', 'armGroupLabels': ['LLM assistance group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '100000', 'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Beijing Luhe Hospital affiliated to Capital Medical University', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'zip': '100053', 'city': 'Beijing', 'state': 'Beijing Municipality', 'country': 'China', 'facility': 'Xuanwu Hospital Capital Medical University', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'zip': '473000', 'city': 'Nanyang', 'state': 'Henan', 'country': 'China', 'facility': 'Nanyang Central Hospital', 'geoPoint': {'lat': 33.00524, 'lon': 112.54659}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Capital Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Ji Xunming,MD,PhD', 'investigatorAffiliation': 'Capital Medical University'}}}}