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

NCT07006961 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 125

Last updated 2025-06-06

No results posted yet for this study

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.

Conditions

  • Avoidant Restrictive Food Intake Disorder

Interventions

OTHER

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.

Sponsors & Collaborators

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
NONE
Model
CROSSOVER

Eligibility

Min Age
3 Years
Max Age
17 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-15
Primary Completion
2025-10-31
Completion
2025-10-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07006961 on ClinicalTrials.gov