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CTEPH Differential Diagnosis Assisted by Enhanced Large Language Model Support
Sponsor: China-Japan Friendship Hospital
Summary
This randomized controlled trial aims to evaluate whether a locked large language model (LLM)-based clinical decision support system improves the differential diagnosis of pulmonary artery filling defects among physicians who do not specialize in pulmonary vascular diseases. Participants will evaluate 30 de-identified clinical cases involving chronic thromboembolic pulmonary hypertension (CTEPH), acute pulmonary embolism (PE), and pulmonary artery sarcoma (PAS), with or without LLM assistance. The primary outcome is sensitivity for identifying CTEPH, while secondary outcomes include overall diagnostic accuracy, time to diagnosis, appropriateness of referral recommendations, and AI-related diagnostic errors.
Official title: Large Language Model-Assisted Differential Diagnosis of Pulmonary Artery Filling Defects: A Stratified Randomized Controlled Trial
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
60
Start Date
2026-10-01
Completion Date
2027-12-31
Last Updated
2026-10-29
Healthy Volunteers
No
Conditions
Interventions
Locked CTEPH-LLM Diagnostic Decision Support
A locked LLM-based decision support system will process the same staged clinical information reviewed by participating clinicians. For each case and stage, the system will display a pre-generated Top-1 diagnosis among CTEPH, acute PE, and PAS; key evidence supporting and opposing that diagnosis; and a referral recommendation. Before randomization, the model name and version, call date, system and user prompts, input fields, context organization, sampling parameters, output format, risk thresholds, and all case-stage outputs will be locked. Outputs will not be selected, repeated, or modified on the basis of participant responses during the study.
Locations (1)
China-Japan Friendship Hospital
Beijing, China