Artificial Intelligence for Rare Disease Diagnosis

June 3, 2026 updated by: Shuyang Zhang, MD, PhD, Peking Union Medical College Hospital

A Multicentre, Randomised Diagnostic Accuracy Study Evaluating AI Assisted Diagnosis of Rare Diseases

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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.

Study Type

Interventional

Enrollment (Estimated)

150

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
      • Beijing, China
        • Peking Union Medical College Hospital
        • Contact:
      • Cangzhou, China
        • Cangzhou Central Hospital
        • Contact:
      • Changchun, China
        • Changchun Sacred Heart Hospital
        • Contact:
      • Dongguan, China
        • Dongguan People's Hospital
        • Contact:
      • Foshan, China
        • First People's Hospital of Foshan
        • Contact:
      • Guiyang, China
        • Guizhou Provincial People's Hospital
        • Contact:
      • Jilin, China
        • Jilin Central General Hospital
        • Contact:
      • Kunming, China
        • The First People's Hospital of Yunnan Province
        • Contact:
      • Lhasa, China
        • Tibet Autonomous Region People's Hospital
        • Contact:
      • Tianjin, China
        • Tianjin Children's Hospital
        • Contact:
      • Wuhai, China
        • Wuhai People's Hospital
        • Contact:
      • Xining, China
        • Qinghai Provincial People's Hospital
        • Contact:
      • Zhangzhou, China
        • Zhangzhou Municipal Hospital of Fujian Province
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Crossover Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Intervention Arm
Physicians complete assigned diagnostic tasks with the assistance of AI system in addition to conventional clinical resources.
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.
No Intervention: Control Arm
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Top-3 Diagnostic Accuracy
Time Frame: 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.
Up to 60 minutes per case (from case presentation to diagnostic report submission).

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnosis Time per Case
Time Frame: 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.
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).
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.
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 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.
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).
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.
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).
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.
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).
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.
Up to 60 minutes per case (upon completion of each case reading).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Peking Union Medical College Hospital

Collaborators

Guizhou Provincial People's Hospital
First People's Hospital of Foshan
Dongguan People's Hospital
Qinghai People's Hospital
Cangzhou Central Hospital
The First People's Hospital of Yunnan
Tibet Autonomous Region People's Hospital
Tianjin Children's Hospital
Zhangzhou Municipal Hospital of Fujian Province

Investigators

  • Principal Investigator: Shuyang Zhang, Peking Union Medical College Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 20, 2026

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

June 1, 2027

Study Registration Dates

First Submitted

May 28, 2026

First Submitted That Met QC Criteria

June 3, 2026

First Posted (Actual)

June 4, 2026

Study Record Updates

Last Update Posted (Actual)

June 4, 2026

Last Update Submitted That Met QC Criteria

June 3, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • PUMCH I-23PJ948

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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