Rare Diseases, Rare Disorders
Conditions
Keywords
Rare Diseases, Artificial Intelligence, Clinical Decision Support System (CDSS), Diagnosis
Brief summary
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.
Detailed description
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.
Interventions
OTHERAI-Assisted Diagnosis
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.
Sponsors
Peking Union Medical College Hospital
Cangzhou Central Hospital
Zhangzhou Municipal Hospital of Fujian Province
Dongguan People's Hospital
First People's Hospital of Foshan
Tibet Autonomous Region People's Hospital
Guizhou Provincial People's Hospital
Tianjin Children's Hospital
The First People's Hospital of Yunnan
Qinghai People's Hospital
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* 1\. Licensed physicians at the junior or senior level affiliated with internal medicine, neurology, pediatrics, and rare disease-related departments. * 2\. Willingness to provide written informed consent, adhere to trial protocols, and complete all required pre-study training prior to enrollment.
Exclusion criteria
* 1\. Prior exposure to any of the clinical cases included in the study case library. * 2\. Direct participation in the design or development of the AI model.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Top-3 Diagnostic Accuracy | Up to 60 minutes per case (from case presentation to diagnostic report submission). | The percentage of definitive diagnosis is included within the physician's top 3 choices. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Diagnosis Time per Case | Up to 60 minutes per case (from case presentation to diagnostic report submission). | Elapsed time from initial case presentation to final diagnostic report submission, recorded automatically via system logs. |
| Workup Plan Quality | Up to 60 minutes per case (from case presentation to diagnostic report submission). | 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. |
| Physician Reported Usability of the AI-Assisted Diagnostic System | Up to 60 minutes per case (upon completion of each case reading). | 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. |
| Physician Reported Workload | Up to 60 minutes per case (upon completion of each case reading). | 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. |
| Physician Satisfaction | Up to 60 minutes per case (upon completion of each case reading). | 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. |
| Physician Intention to Adopt AI-Assisted Diagnostic Support | Up to 60 minutes per case (upon completion of each case reading). | 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. |
Countries
China
Contacts
CONTACTShuyang Zhang
PRINCIPAL_INVESTIGATORShuyang Zhang
Peking Union Medical College Hospital
Outcome results
None listed