Abstract
The integration of artificial intelligence (AI) and machine learning (ML) has shown potential for various applications in the medical field, particularly for diagnosing and managing chronic diseases among children and adolescents. This systematic review aims to comprehensively analyze and synthesize research on the use of AI for monitoring, guiding, and assisting pediatric patients with chronic diseases. Five major electronic databases were searched (Medline, Scopus, PsycINFO, ACM, Web of Science), along with manual searches of gray literature, personal archives, and reference lists of relevant papers. All original studies as well as conference abstracts and proceedings, focusing on AI applications for pediatric chronic disease care were included. Thirty-one studies met the inclusion criteria. We extracted AI method used, study design, population, intervention, and main results. Two researchers independently extracted data and resolved discrepancies through discussion. AI applications are diverse, encompassing, e.g., disease classification, outcome prediction, or decision support. AI generally performed well, though most models were tested on retrospective data. AI-based tools have shown promise in mental health analysis, e.g., by using speech sampling or social media data to predict therapy outcomes for various chronic conditions. CONCLUSIONS: While AI holds potential in pediatric chronic disease care, most reviewed studies are small-scale research projects. Prospective clinical implementations are needed to validate its effectiveness in real-world scenarios. Ethical considerations, cultural influences, and stakeholder attitudes should be integrated into future research. WHAT IS KNOWN: • Artificial Intelligence (AI) will play a more dominant role in medicine and healthcare in the future and many applications are already being developed. WHAT IS NEW: • Our review provides an overview on how AI-driven systems might be able to support children and adolescents with chronic illnesses. • While many applications are being researched, few have been tested on real-world, prospective, clinical data.