Artificial intelligence for child health: current capabilities and the next frontier
Abstract
Artificial intelligence (AI) is reshaping paediatric healthcare, offering new capabilities across diagnosis, monitoring and treatment personalisation. Modern AI systems integrate multimodal data, including imaging, genomics, electronic health records, wearable sensors, environmental exposures and patient-reported outcomes, to generate insights tailored to children's developmental, physiological and psychosocial needs. Advances in machine learning, deep learning, natural language processing, computer vision and generative models are enabling earlier detection of rare diseases, dynamic risk stratification and personalised care pathways. Emerging technologies such as digital twins simulate individual disease trajectories and treatment responses, reducing reliance on traditional trial designs and supporting anticipatory, precision care.The next frontier of AI in healthcare includes adaptive decision support powered by reinforcement learning and advanced time-series modelling, allowing systems to respond to real-time physiological changes and support complex sequential decisions in areas such as ventilation, insulin dosing, deterioration prediction and medication titration. Parallel progress in remote monitoring and smart sensors is shifting care from hospitals to homes, supporting long-term condition management and reducing avoidable admissions. Causal AI offers further potential by uncovering true cause-and-effect relationships, enabling clinicians to understand why…
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