Artificial Intelligence for Rare Disease Diagnosis

Chinese Academy of Medical Sciences & Peking Union Medical College

Status: Begins enrollment this month

Conditions: Rare Disorders; Rare Diseases

Treatments: Other: AI-Assisted Diagnosis

Study type: Interventional

Funder types: Other

Identifiers

NCT07625436

PUMCH I-23PJ948

Details and patient eligibility

About

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.

Full 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.

Enrollment: 150 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Crossover Assignment

Masking: Single Blind

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150 participants in 2 patient groups

Intervention Arm

Experimental group

Description:

Physicians complete assigned diagnostic tasks with the assistance of AI system in addition to conventional clinical resources.

Treatment:

Other: AI-Assisted Diagnosis

Control Arm

No Intervention group

Description:

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.