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Scalable, Clinician-Supervised Generative-AI Food-Chaining Assistant for Pediatric ARFID
Sponsor: Boston Children's Hospital
Summary
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.
Official title: Scalable, Clinician-Supervised Generative-AI Food-Chaining Assistant for Pediatric ARFID: A Pilot Randomized Controlled Trial
Key Details
Gender
All
Age Range
3 Years - 17 Years
Study Type
INTERVENTIONAL
Enrollment
125
Start Date
2025-06-15
Completion Date
2025-10-31
Last Updated
2025-06-06
Healthy Volunteers
No
Interventions
Generative AI-based food chaining device
Our group has developed the only generative-AI tool that produces allergy-safe food-chaining recommendations, but it has not yet been clinically tested. This proposal builds on that proof of concept to evaluate its effectiveness in a broader pediatric ARFID population.