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Legal guardian co-signs consent for minors or individuals lacking legal capacity.\n* 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.\n* First presentation to the enrolling institution for the current condition, with no prior records in the institutional HIS or outpatient system.\n* No prior genetic testing related to the current condition; no results or reports available.\n* Written informed consent provided voluntarily by patient or legal guardian, with commitment and ability to complete structured follow-up.\n\nPatient Exclusion Criteria:\n\n* Confirmed diagnosis (clinical, pathological, or molecular) explaining the primary symptoms.\n* Emergency presentation, critical illness, or any condition incompatible with trial participation.\n* Neither patient nor legally authorised proxy able to complete follow-up.\n* Concurrent enrollment in another interventional study with diagnostic accuracy or genetic testing yield as a primary endpoint.\n* Prior use of another AI system has already yielded a confirmed diagnosis for the current condition.\n\nPhysician Inclusion Criteria\n\n* Licensed physician in internal medicine, neurology, pediatrics, general medicine, rare disease, or a related specialty.\n* ≥2 years of clinical practice; competent to manage rare disease patients; stratified into junior or senior tier.\n* Voluntary participation with written informed consent.\n\nPhysician Exclusion Criteria\n\n* No longer in clinical practice, or unable to fulfill required outpatient duties during the study period.\n* Unwilling to provide informed consent or to permit protocol-required collection of consultation and questionnaire data.\n* Currently enrolled in another AI-assisted clinical workflow, or expected to be unable to comply with the procedures.","ALL","0 Years",{"count":177,"type":178},1056,"ESTIMATED","INTERVENTIONAL",[181],"NA","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.",[184,185],"Rare Disorders","Rare Diseases",[187,188,189,190,191],"rare diseases","AI","LLM","diagnosis","cost-effectiveness","NOT_YET_RECRUITING","2026-07-30",{"date":195,"type":196},"2026-07-31","ACTUAL",{"date":198,"type":178},"2026-08-01",{"date":200,"type":178},"2027-12-01",{"name":5,"class":6},13]