AI Empowers Autism Nutritional Care Solutions: A Family-Centred Innovation - Maspul, Kurniawan Arif; Ardhin, Muhammad (Indonesian Journal Publisher, 2025)
Autism Spectrum Disorder (ASD), affecting 1 in 36 U.S. children, presents profound feeding challenges—extreme food selectivity, sensory aversions, and mealtime meltdowns—that strain caregivers and jeopardize child health. Over 70% of caregivers report clinically significant stress, exacerbated by healthcare systems offering generic, neurologically disconnected dietary advice. This study introduces Feeding Assistant for Children with ASD, a personalized AI assistant grounded in the SPELL framework (Structure, Positive approaches, Empathy, Low arousal, Links) and health behavior theories (Social Cognitive Theory, Ecological Systems Theory). Built via participatory co-design with families, clinicians, and AI engineers, the tool delivers culturally sensitive, context-aware nutrition planning and feeding strategies. Over six weeks, a case study with a neurodiverse family demonstrated reduced caregiver stress (quantified via validated scales) and fewer mealtime refusals, validated through mixed-method analysis. The AI’s empathetic tone, structured routines, and sensory-sensitive recommendations align with neurobiological insights, such as atypical connectivity in the amygdala and insula. Ethical safeguards—privacy protocols, bias mitigation, and cultural inclusivity—ensure dignity-centered care. Results advocate for systemic integration of AI into national health strategies, emphasizing equitable access, interdisciplinary training, and policy reforms to bridge gaps in ASD care. This AI model redefines technology's function as both a practical ally and an emotional anchor, building resilience in neurodiverse households.