Peer-reviewed article
Artificial intelligence in pediatric diabetes: clinical benefits, developmental risks, and ethical challenges.
Khalil H, Lynch JL, Sowan A, Ortega A, Zaitoon H ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This review examines artificial intelligence applications in pediatric diabetes care, including automated insulin delivery systems and educational chatbots. Medical AI technologies improve blood glucose control and reduce management burden for children with diabetes. However, the review identifies significant concerns about child development, psychological effects, data privacy, and unequal access that require further research beyond glycemic outcomes.
Key points
- Automated insulin delivery systems improve glycemic control and increase time in range, now recommended as standard care for children with type 1 diabetes
- Children with diabetes experience dual exposure to both medical AI and consumer AI, creating complex developmental and psychological challenges
- Key concerns include increased screen exposure, continuous surveillance, caregiver anxiety, data privacy vulnerabilities, algorithmic bias, and inequitable access
- The review introduces the 'double-edged algorithm' concept, noting that while medical AI improves glucose management, broader developmental effects remain insufficiently understood
- Research gaps exist regarding long-term impacts on child autonomy, family functioning, health equity, and self-management skills beyond glucose metrics
Limitations
This is a narrative review synthesising existing literature rather than original research, so it cannot provide definitive causal evidence. The document does not evaluate the actual implementation of these technologies in specific populations or settings, limiting understanding of real-world effectiveness and harms.
Abstract
Artificial intelligence (AI) is rapidly transforming both pediatric healthcare and everyday childhood experiences. Children with diabetes represent a unique population at the intersection of medical AI, including automated insulin delivery (AID) systems, clinical decision support tools, and predictive glucose monitoring, and consumer AI, such as chatbots and AI-based educational platforms. This paper examines the benefits, risks, and unresolved dilemmas associated with this dual AI exposure. This narrative review addresses medical and consumer AI applications in pediatric diabetes. The literature search included three databases and incorporated MeSH terms. Evidence synthesis focused on four main domains: clinical efficacy, developmental and psychosocial impact, ethical and governance considerations, and societal implications. Medical AI technologies, particularly AID systems, improve glycemic control, increase time in range, and reduce diabetes-management burden. Consumer AI applications offer emerging evidence for personalized diabetes education and disease-management support. However, dual AI exposure introduces a complex spectrum of developmental, psychological, ethical, and governance-related challenges, including increased screen exposure, continuous surveillance, caregiver anxiety, altered psychosocial development, data privacy vulnerabilities, algorithmic bias, and inequitable access. The increasing use of generative AI introduces additional potential concerns related to misinformation, psychological dependence, and altered health behaviors. Conclusion Medical AI technologies are increasingly integrated into pediatric diabetes care, with the strongest evidence supporting AID. However, the broader developmental, psychosocial, ethical, and societal effects of sustained exposure to medical and consumer AI remain insufficiently understood. Future research should extend beyond glycemic metrics to evaluate child development, autonomy, family functioning, privacy, health equity, and long-term self-management. What is known • AI-driven diabetes technologies, particularly automated insulin delivery systems, improve 69 glycemic outcomes and are now recommended as the standard of care for children with 70 type 1 diabetes. What is new • Children with diabetes face unique developmental, ethical, and psychosocial challenges from both medical and consumer AI, extending beyond glucose management to surveillance, autonomy, screen exposure, and child development. • This review introduces the "double-edged algorithm" concept and proposes shifting from "quantifying the child" toward "supporting the caregiver," while highlighting evidence gaps on the long-term developmental effects of AI-assisted childhood.