AI-assisted Rare Disease Diagnosis
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age0+
SponsorPeking Union Medical College Hospital
About this trial
A multicentre randomised controlled trial evaluating whether a rare-disease diagnostic large language model can improve diagnostic quality, efficiency, and health-economic outcomes for physicians managing patients with suspected rare or diagnostically unresolved disease.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Any age. Legal guardian co-signs consent for minors or individuals lacking legal capacity.
Diagnostically unresolved or suspected rare disease, with at least one prior complete clinical evaluation at a secondary-level or higher institution yielding no confirmed explanatory diagnosis.
First presentation to the enrolling institution for the current condition, with no prior records in the institutional HIS or outpatient system.
No prior genetic testing related to the current condition; no results or reports available.
Disqualifiers
Confirmed diagnosis (clinical, pathological, or molecular) explaining the primary symptoms.
Emergency presentation, critical illness, or any condition incompatible with trial participation.
Neither patient nor legally authorised proxy able to complete follow-up.
Concurrent enrollment in another interventional study with diagnostic accuracy or genetic testing yield as a primary endpoint.
Trial design
Design model
Parallel
Treatments tested in this trial
AI system
Other interventionThe study AI system will be used to provide diagnostic support during the clinical encounter, including structuring relevant clinical information, generating a clinical analysis, and suggesting candidate diagnoses for review by the treating physician.
Treatment groups
1,056 Participants
are divided into 2 treatment groupsGroup A: AI systemExperimental treatment 1 intervention
Group B: Standard of careNo intervention 0 interventions
Trial outcomes
Primary outcomes
1
Overall Correct Diagnostic Yield
The proportion of all randomised patients whose clinical diagnosis by the end of follow-up is concordant with the blinded-adjudicated final reference diagnosis determined by an independent committee.
Time frame
From the first visit to final reference diagnosis adjudication, an average of 8 weeks.Secondary outcomes
1
Candidate Diagnostic Accuracy
The agreement between physician-provided candidate diagnoses in the the initial consultation and the independently adjudicated reference diagnosis.
Time frame
From the first visit to final reference diagnosis adjudication, an average of 8 weeks.2
Molecular Diagnostic Yield
The proportion of all randomized patients in whom genetic testing performed as part of the clinical diagnostic pathway identifies a clinically relevant molecular finding that is confirmed through independent genetics review.
Time frame
From the first visit to final reference diagnosis adjudication, an average of 8 weeks.3
Time to a Correct Diagnosis
The number of days from the first study visit to the first physician-assigned diagnosis that is subsequently confirmed as concordant with the independently adjudicated reference diagnosis.
Time frame
From enrollment to the end of follow-up, up to 8 weeks.4
Appropriate Genetic Testing Recommendation Rate
The proportion of randomized patients for whom physician-recommended genetic testing is concordant with the indication determined by an independent genetics adjudication committee.
Time frame
From the initial consultation to genetic testing indication adjudication, approximately 8 weeksSponsors and contacts
Click on the lead sponsor to view all of their trials.
Peking Union Medical College Hospital
Lead sponsor
Cangzhou Central Hospital
Collaborator
Zhangzhou Municipal Hospital
Collaborator
Dongguan People's Hospital
Collaborator
First People's Hospital of Foshan
Collaborator
Guizhou Provincial People's Hospital
Collaborator
Tianjin Children's Hospital
Collaborator
The First People's Hospital of Yunnan
Collaborator
Qinghai People's Hospital
Collaborator
Source ClinicalTrials.gov
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