Scalable, Clinician-Supervised Generative-AI Food-Chaining Assistant for Pediatric ARFID: A Pilot Randomized Controlled Trial

ARFID is an eating disorder marked by highly selective eating that can lead to significant nutrient and energy deficiencies. Patients may show low interest in food because of certain tastes, textures, or smells; have chronically low appetite; and/or avoid eating out of fear (e.g., choking, vomiting), resulting in inadequate oral intake. A recent systematic review estimates the prevalence of ARFID in children and adolescents at 0.3-15.5%. Unlike anorexia nervosa or bulimia nervosa, body image concerns do not drive ARFID. Comorbidities-including autism spectrum disorder, attention-deficit/hyperactivity disorder, anxiety, and depression-are common.

Care is provided either by frontline clinicians (e.g., pediatricians, community dietitians) or multidisciplinary specialty teams. Outpatient management may combine cognitive-behavioral therapy adapted for ARFID (CBT-AR), family-based or exposure therapy, and dietitian-guided food-repertoire expansion via food chaining. Food chaining gradually introduces foods similar to an individual's "safe" items, creating an actionable, stepwise plan that accumulates meaningful dietary change.

Large-language models such as ChatGPT are increasingly used in health care. By constraining model output via an application programming interface (API), these models can power domain-specific chatbots. While ChatGPT excels at general queries, it is not inherently expert in ARFID or food chaining; however, with appropriate fine-tuning, LLMs could deliver scalable, personalized, high-quality chaining plans.

AI tools still pose risks. In 2023, the National Eating Disorders Association released "Tessa," an AI chatbot intended for eating-disorder support. Although a clinical trial showed modest reductions in weight/shape concerns at three and six months (d ≈ -0.2; p ≈ 0.03-0.04), the bot later provided harmful weight-loss advice, recommending calorie restriction and other disordered behaviors, leading to its shutdown. This case underscores the need for rigorous clinician oversight during early deployment of medical chatbots.

This research 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.