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

Overview

A multicentre randomised controlled trial evaluating whether a rare-disease diagnostic large language model can improve diagnostic quality, efficiency, and health-economic outcomes for physicians managing patients with suspected rare or diagnostically unresolved disease.

Detailed description

Rare disease patients commonly experience prolonged diagnostic odysseys rooted in limited rare disease recognition, phenotypic heterogeneity, and dispersed diagnostic clues. Diagnostic decision-support large language models may improve first-visit consultations by integrating prior records, generating structured analyses, and proposing candidate diagnoses, thereby shortening diagnostic pathways and improving appropriate genetic testing referral.

Participating physicians will provide care under both AI-assisted and standard diagnostic workflows. Eligible patients will be individually randomised to receive either AI-assisted diagnostic support or standard clinical practice.

In the intervention arm, physicians will have diagnostic support from AI when seeing patients. In the control arm, patients are seen under standard hospital workflow without any generative AI tools. Outcomes adjudicated by an independent Expert Committee blinded to arm assignment; adjudicators access no AI-generated materials.

A prospective within-trial economic evaluation will be conducted alongside the randomized trial. Healthcare resource use and costs associated with the diagnostic pathway will be collected.

Interventions

• Other AI system
  The study AI system will be used to provide diagnostic support during the clinical encounter, including structuring relevant clinical information, generating a clinical analysis, and suggesting candidate diagnoses for review by the treating physician.

Primary outcome measures

• Overall Correct Diagnostic Yield [Time frame: From the first visit to final reference diagnosis adjudication, an average of 8 weeks.]

Secondary outcome measures (7)

• Candidate Diagnostic Accuracy [Time frame: From the first visit to final reference diagnosis adjudication, an average of 8 weeks.]
• Molecular Diagnostic Yield [Time frame: From the first visit to final reference diagnosis adjudication, an average of 8 weeks.]
• Time to a Correct Diagnosis [Time frame: From enrollment to the end of follow-up, up to 8 weeks.]
• Appropriate Genetic Testing Recommendation Rate [Time frame: From the initial consultation to genetic testing indication adjudication, approximately 8 weeks]
• Duration of the Initial Physician Consultation [Time frame: Assessed at each consultation (day 1), within 1 day.]
• Physician-Reported Experience [Time frame: Assessed at each consultation (day 1), within 1 day.]
• Patient-Reported Experience [Time frame: Assessed at each consultation (day 1), within 1 day.]

Eligibility 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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Randomized

Model

Parallel assignment

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: NCT07650799 · PUMCH I-26PJ0002

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗