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.