Digital health and artificial intelligence in paediatric respiratory medicine: what clinicians need to know and what is coming next

Abstract

Digital health and artificial intelligence (AI) are increasingly part of paediatric respiratory clinics, from home-based symptom monitoring and remote consultations to AI-supported test interpretation and large language model (LLM)-supported communication. These tools promise to capture real-world disease and treatment patterns, support self-management, and enhance clinical decisions. Yet for most of these tools, the paediatric evidence base remains limited and fragmented, and the clinical benefit observed in practice is often modest. What has emerged is that effectiveness depends less on the technology itself than on how it fits into the child–parent–clinician triad, on its usability in daily family life, and on its impact on clinical workflow and on equity. Paediatric datasets remain small, heterogeneous and developmental, making extrapolation from adult evidence risky. Over the next 3 years, progress is likely to consolidate rather than expand, with paediatric-specific validation, regulation and routine integration. Clinicians will play a central role: deciding what to adopt, what to question, and what to govern.