Rare Diseases, Rare Disorders
Conditions
Keywords
rare diseases, AI, LLM, diagnosis, cost-effectiveness
Brief summary
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
OTHERAI 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.
Sponsors
Peking Union Medical College Hospital
Cangzhou Central Hospital
Zhangzhou Municipal Hospital
Dongguan People's Hospital
First People's Hospital of Foshan
Guizhou Provincial People's Hospital
Tianjin Children's Hospital
The First People's Hospital of Yunnan
Qinghai People's Hospital
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Outcomes Assessor)
Masking description
Patient level: open label. Treating physicians: open label. Outcome adjudicators (independent expert committee) and statisticians: blinded. Adjudicators do not know arm assignment and are not shown any AI-generated or AI-attributed material; statistical analysis coded A/B until unblinding.
Intervention model description
This is a multicentre, prospective, parallel controlled study. Eligible patients will be assigned to either an AI-assisted diagnostic workflow or a standard diagnostic workflow. Participating physicians will provide care under both study conditions.
Eligibility
Sex/Gender
ALL
Age
0 Years to No maximum
Healthy volunteers
No
Inclusion 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Overall Correct Diagnostic Yield | From the first visit to final reference diagnosis adjudication, an average of 8 weeks. | The proportion of all randomised patients whose clinical diagnosis by the end of follow-up is concordant with the blinded-adjudicated final reference diagnosis determined by an independent committee. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Candidate Diagnostic Accuracy | From the first visit to final reference diagnosis adjudication, an average of 8 weeks. | The agreement between physician-provided candidate diagnoses in the the initial consultation and the independently adjudicated reference diagnosis. |
| Molecular Diagnostic Yield | From the first visit to final reference diagnosis adjudication, an average of 8 weeks. | The proportion of all randomized patients in whom genetic testing performed as part of the clinical diagnostic pathway identifies a clinically relevant molecular finding that is confirmed through independent genetics review. |
| Time to a Correct Diagnosis | From enrollment to the end of follow-up, up to 8 weeks. | The number of days from the first study visit to the first physician-assigned diagnosis that is subsequently confirmed as concordant with the independently adjudicated reference diagnosis. |
| Appropriate Genetic Testing Recommendation Rate | From the initial consultation to genetic testing indication adjudication, approximately 8 weeks | The proportion of randomized patients for whom physician-recommended genetic testing is concordant with the indication determined by an independent genetics adjudication committee. |
| Duration of the Initial Physician Consultation | Assessed at each consultation (day 1), within 1 day. | In-room consultation time will be recorded, measured, and compared between arms. |
| Physician-Reported Experience | Assessed at each consultation (day 1), within 1 day. | Physicians will assess their experience of the diagnostic workflow. Responses will be recorded using a standardized rating scale (range 1-5, where higher scores indicate more positive experience). |
| Patient-Reported Experience | Assessed at each consultation (day 1), within 1 day. | Patients will assess their experience of the diagnostic workflow. Responses will be recorded using a standardized rating scale (range 1-5, where higher scores indicate more positive experience). |
Countries
China
Contacts
CONTACTShuyang Zhang, MD, PhD
PRINCIPAL_INVESTIGATORShuyang Zhang, MD, PhD
Peking Union Medical College Hospital
Outcome results
None listed