• AI-assisted Rare Disease Diagnosis

  NCT07650799

  Not yet recruitingInterventionalNot ApplicableUpdated Jul 31, 2026
• Real-world of AI in Diagnosing Retinal Diseases

  NCT05981950

  RecruitingObservationalUpdated Aug 8, 2023
• AI-Assisted Medical Decision-Making

  NCT06846229

  RecruitingObservationalUpdated Mar 3, 2025
• Validation of the Utility of Rare Disease Intelligence Platform

  NCT02748044

  CompletedInterventionalNot ApplicableUpdated Apr 22, 2016
• AI Models in Clinical Pathology Diagnosis: A Multicenter RCT

  NCT07408167

  Not yet recruitingInterventionalNot ApplicableUpdated Feb 13, 2026
• Artificial Intelligence for Detecting Retinal Diseases

  NCT04678375

  CompletedObservationalUpdated Apr 15, 2021

ClinicalTrials.gov ID

NCT07625436

Lead Sponsor

Peking Union Medical College HospitalOTHER

Overall Status

Not yet recruiting

Study Type

Interventional

Phase

Not Applicable

Enrollment

150Estimated

Last Update Posted

Jun 4, 2026Actual

Start Date

Jun 20, 2026Estimated

Primary Completion Date

Dec 1, 2026Estimated

Completion Date

Jun 1, 2027Estimated

Official Title

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

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.

• Rare Disorders
• Rare Diseases

• AI-Assisted Diagnosis

Ages Eligible for Study

Adult (18-64)Older Adult (65+)

Sexes Eligible for Study

All

Accepts Healthy Volunteers

Yes

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.

Primary Purpose

Diagnostic

Allocation

Randomized

Interventional Model

Crossover Assignment

Masking

Single

Lead Sponsor

Peking Union Medical College HospitalOTHER

Collaborators

Cangzhou Central HospitalOTHER

Zhangzhou Municipal Hospital of Fujian ProvinceOTHER

Dongguan People's HospitalOTHER_GOV

First People's Hospital of FoshanOTHER

Tibet Autonomous Region People's HospitalOTHER

Guizhou Provincial People's HospitalOTHER

Tianjin Children's HospitalOTHER

The First People's Hospital of YunnanOTHER

Qinghai People's HospitalOTHER

czszxyyirb@163.com+86-0317-2075013

ccsx_net2008@126.com+86-0431-88711699

dgrmyyirb@163.com+86-0769-28637333

jinye0320@163.com+86-0757-83833633

sytsb606@sina.com+86-0851-85937094

jlszxyy@163.com+86-0432-62456181

wudong@pumch.cn+86-0891-6371322

setyyyb@tj.gov.cn022-87787101

whsrmyybgs@163.com+86-0473-2035041

403252559@qq.com+86-0971-8177911