Clinical trial Recruiting at 3 sites Interventional Ages 0 and up

AI-assisted Rare-Disease Diagnosis

Official title: A Multicentre Randomised Controlled Trial of LLM-Assisted Diagnostic Support in Patients With Suspected Rare or Diagnostically Unresolved Disease

Run by Peking Union Medical College Hospital · NCT07650799 · Data updated 2026-11-12

A multicenter randomized test to see if an AI diagnostic tool can help doctors make faster, more accurate rare-disease diagnoses for patients with unresolved symptoms.

Quick facts

Study typeInterventional
Ages0 Years and up
SexAll
Enrollment1056 (estimated)
SponsorPeking Union Medical College Hospital
Academic / other
Locations12 sites (Cangzhou and 11 other locations)
StatusRecruiting
StartedAugust 1, 2026
Main results expectedJuly 1, 2027
Study endsDecember 1, 2027
Registry record updatedOctober 13, 2026
Trial IDNCT07650799 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Ideal candidates are patients of any age with diagnostically unresolved or suspected rare diseases, lacking prior genetic testing, and presenting for the first time at a participating center.

Probably not a fit

Patients with already confirmed diagnoses, emergency conditions, or those unable to complete follow-up may not benefit from the AI-assisted approach.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This trial compares AI-assisted diagnostic support with standard care in patients with suspected or unresolved rare diseases. Physicians use the AI system during patient visits in the intervention arm, while the control arm follows usual workflow without AI tools. Outcomes are adjudicated by an independent committee blinded to arm assignment, and a parallel economic evaluation will track health-care resource use and costs. Eligible patients include individuals of any age with no prior genetic testing and no confirmed diagnosis, seen for the current condition for the first time at participating centers. The study aims to improve diagnostic quality and efficiency and to assess cost-effectiveness of the AI approach in rare-disease care.

Why it matters

Potential benefit: If successful, the AI tool could shorten diagnostic pathways and improve access to appropriate genetic testing for patients with suspected rare diseases.

How similar studies have gone: Previous studies in AI-assisted diagnosis for complex conditions have shown promise but robust data in rare diseases remain limited and ongoing.

Who can join: the full rules

Show full inclusion / exclusion criteria
Patient Inclusion Criteria:

* Any age. Legal guardian co-signs consent for minors or individuals lacking legal capacity.
* Diagnostically unresolved or suspected rare disease, with at least one prior complete clinical evaluation at a secondary-level or higher institution yielding no confirmed explanatory diagnosis.
* First presentation to the enrolling institution for the current condition, with no prior records in the institutional HIS or outpatient system.
* No prior genetic testing related to the current condition; no results or reports available.
* Written informed consent provided voluntarily by patient or legal guardian, with commitment and ability to complete structured follow-up.

Patient Exclusion Criteria:

* Confirmed diagnosis (clinical, pathological, or molecular) explaining the primary symptoms.
* Emergency presentation, critical illness, or any condition incompatible with trial participation.
* Neither patient nor legally authorised proxy able to complete follow-up.
* Concurrent enrollment in another interventional study with diagnostic accuracy or genetic testing yield as a primary endpoint.
* Prior use of another AI system has already yielded a confirmed diagnosis for the current condition.

Physician Inclusion Criteria

* Licensed physician in internal medicine, neurology, pediatrics, general medicine, rare disease, or a related specialty.
* ≥2 years of clinical practice; competent to manage rare disease patients; stratified into junior or senior tier.
* Voluntary participation with written informed consent.

Physician Exclusion Criteria

* No longer in clinical practice, or unable to fulfill required outpatient duties during the study period.
* Unwilling to provide informed consent or to permit protocol-required collection of consultation and questionnaire data.
* Currently enrolled in another AI-assisted clinical workflow, or expected to be unable to comply with the procedures.

Where it's running

  • Jilin Central General Hospital
    Jilin City, China
    Recruiting
  • Tianshui 407 Hospital
    Tianshui, China
    Recruiting
  • Qinghai Provincial People's Hospital
    Xining, China
    Recruiting
  • Cangzhou Central Hospital
    Cangzhou, China
    Not yet recruiting
  • Dongguan People's Hospital
    Dongguan, China
    Not yet recruiting
  • First People's Hospital of Foshan
    Foshan, China
    Not yet recruiting
Show 6 more locations
  • Guizhou Provincial People's Hospital
    Guiyang, China
    Not yet recruiting
  • The First People's Hospital of Yunnan Province
    Kunming, China
    Not yet recruiting
  • Tianjin Children's Hospital
    Tianjin, China
    Not yet recruiting
  • Tianshui 407 Hospital
    Tianshui, China
    Not yet recruiting
  • Wuhai People's Hospital
    Wuhai, China
    Not yet recruiting
  • Zhangzhou Municipal Hospital of Fujian Province
    Zhangzhou, China
    Not yet recruiting

Study contacts

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.