An interventional study of AI-Assisted Diagnosis in Rare Disorders and Rare Diseases, sponsored by Peking Union Medical College Hospital. Not yet recruiting at 13 sites in China. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-06-04.
Sponsored by Peking Union Medical College Hospital · Not applicable, Interventional, and Diagnostic
A multicentre, randomised diagnostic accuracy study to evaluate whether the rare disease-specific AI can improve diagnostic accuracy and efficiency for physicians managing real-world clinical cases.
Rare diseases collectively affect approximately 300 million individuals worldwide. This prolonged diagnostic delay is attributable in large part to the breadth of over 7,000 recognized rare conditions, which far exceeds the clinical exposure of any individual physician. A rare disease-specific diagnostic AI was developed by Peking Union Medical College Hospital (PUMCH), supporting differential diagnosis generation, clinical workup planning, and genomic variant interpretation. A balanced crossover design ensures that each enrolled physician serves as their own control, substantially reducing confounding from inter-reader variability in baseline diagnostic competency. Within each physician, cases are randomly assigned at the case level to either the AI-assisted or unassisted condition, such that each physician reads a subset of cases with AI assistance and the remaining cases without. This within-reader, case-level randomization eliminates the need for a washout period and directly controls for inter-reader differences in baseline diagnostic competency. All cases are collected from real-world clinical settings with independently confirmed gold-standard diagnoses and span a pre-specified spectrum of rare and non-rare disease categories, reflecting the differential diagnostic challenge encountered in routine clinical practice, to ensure diagnostic breadth and clinical representativeness. Physician seniority (junior vs. senior) is incorporated as a pre-specified stratification and subgroup analysis variable. Diagnostic outputs are evaluated by an independent Expert Adjudication Committee, blinded to the assistance condition, using standardized scoring criteria established prior to data collection.
203 studies on the registry are indexed under Rare Diseases; 112 are open to participants now.
This study's planned enrollment of 150 is above the median of 57 across 71 interventional studies indexed under Rare Diseases.
Browse Rare Diseases studies →Peking Union Medical College Hospital is the lead sponsor of 1,115 studies on the registry; 463 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
Physicians complete assigned diagnostic tasks with the assistance of AI system in addition to conventional clinical resources.
Other: AI-Assisted Diagnosis
Physicians complete the assigned diagnostic tasks using conventional clinical resources only (e.g., medical databases and literature), without access to any generative AI tools. This arm reflects routine clinical diagnostic practice.
A rare disease-specific diagnostic AI model is used to accept free text input and assist in rare disease diagnoses. During the experimental condition, physicians may interact with the system freely alongside standard clinical resources to support their diagnostic reasoning.
Top-3 Diagnostic Accuracy
The percentage of definitive diagnosis is included within the physician's top 3 choices.
Time frame: Up to 60 minutes per case (from case presentation to diagnostic report submission).
Diagnosis Time per Case
Elapsed time from initial case presentation to final diagnostic report submission, recorded automatically via system logs.
Time frame: Up to 60 minutes per case (from case presentation to diagnostic report submission).
Workup Plan Quality
Quality score of the clinical workup plan assigned by an independent expert committee using a standardized Likert Scale. Scores range from 1 to 10, with higher scores indicating better workup plan quality.
Time frame: Up to 60 minutes per case (from case presentation to diagnostic report submission).
Physician Reported Usability of the AI-Assisted Diagnostic System
Physician-reported usability of the AI system, assessed after completion of each AI-assisted case reading using a 10-point physician-rated usability scale. Scores range from 1 to 10, with higher scores indicating better system usability.
Time frame: Up to 60 minutes per case (upon completion of each case reading).
Physician Reported Workload
Task-related workload experienced by physicians, assessed after completion of each AI-assisted case reading using a 10-point Physician Workload Likert scale. Scores range from 1 to 10, with higher scores indicating a higher workload.
Time frame: Up to 60 minutes per case (upon completion of each case reading).
Physician Satisfaction
Overall satisfaction of physicians with the diagnostic workflow, assessed after completion of each AI-assisted case reading using a 10-point Satisfaction Likert scale. Scores range from 1 to 10, with higher scores indicating higher satisfaction.
Time frame: Up to 60 minutes per case (upon completion of each case reading).
Physician Intention to Adopt AI-Assisted Diagnostic Support
Physician willingness to integrate AI system into routine clinical practice, assessed after completion of each AI-assisted case reading using a 10-point Adoption Intention Likert scale. Scores range from 1 to 10, with higher scores indicating higher adoption intention.
Time frame: Up to 60 minutes per case (upon completion of each case reading).
This study is not yet recruiting, as verified in Jun 2026. You cannot join it, but the record below documents what was studied.
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Peking Union Medical College Hospital