Artificial Intelligence for Rare Disease Diagnosis
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-Assisted Diagnosis.
- Who it may be relevant to
- Registry conditions: Rare Disorders, Rare Diseases. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
A Multicentre, Randomised Diagnostic Accuracy Study Evaluating AI Assisted Diagnosis of Rare Diseases
Overview
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
- Other AI-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.
Primary outcome measures
- Top-3 Diagnostic Accuracy [Time frame: Up to 60 minutes per case (from case presentation to diagnostic report submission).]
Secondary outcome measures (6)
- Diagnosis Time per Case [Time frame: Up to 60 minutes per case (from case presentation to diagnostic report submission).]
- 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 [Time frame: Up to 60 minutes per case (upon completion of each case reading).]
- Physician Reported Workload [Time frame: Up to 60 minutes per case (upon completion of each case reading).]
- Physician Satisfaction [Time frame: Up to 60 minutes per case (upon completion of each case reading).]
- Physician Intention to Adopt AI-Assisted Diagnostic Support [Time frame: Up to 60 minutes per case (upon completion of each case reading).]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Crossover
- Masking
- Single blind
- Primary purpose
- Diagnostic
Study locations
China · 13 centers
- Peking Union Medical College Hospital — Beijing
- Cangzhou Central Hospital — Cangzhou
- Changchun Sacred Heart Hospital — Changchun
- Dongguan People's Hospital — Dongguan
- First People's Hospital of Foshan — Foshan
- Guizhou Provincial People's Hospital — Guiyang
- Jilin Central General Hospital — Jilin City
- The First People's Hospital of Yunnan Province — Kunming
- … and 5 more centers
Identifiers
NCT: NCT07625436 · PUMCH I-23PJ948