Scalable, Clinician-Supervised Generative-AI Food-Chaining Assistant for Pediatric ARFID

Children with Avoidant/Restrictive Food Intake Disorder (ARFID) often lack access to specialty dietitians, and scalable nutritional guidance/food chaining tools are currently not available. The investigators will evaluate a web-based, clinician-supervised, generative-AI assistant that produces individualized food-chaining plans. Develop an AI assistant that generates ≥15 allergy-safe, evidence-based chaining steps per participant and meets ≥90 % expert agreement for safety/appropriateness. Validate the assistant against gold-standard clinician recommendations (Cohen's κ ≥ 0.80). Test clinical impact in a three-month pilot RCT (n = 96) by comparing change in Nine-Item ARFID Screen (NIAS) scores between intervention and usual-care groups. Hypothesis: AI-generated plans will reduce NIAS scores by ≥3 points relative to controls.