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Artificial intelligence (AI) is transforming pediatric healthcare, offering novel opportunities for early diagnosis, personalized treatment, and more efficient clinical workflows. However, its integration into children's health faces significant challenges due to the unique developmental, biological, and ethical considerations involved. This review explores how AI, leveraging large-scale real-world data such as electronic health records (EHRs), can augment pediatric clinical decision-making, risk stratification, communication, and workflow under human oversight. We examine its current applications and potential to improve pediatric care in areas including disease diagnosis, prediction, prevention, and personalized treatment. Additionally, we evaluate the role of AI in accelerating pediatric drug discovery and in supporting global health and epidemic management for children. Despite these promising advancements, significant barriers, such as data scarcity, ethical dilemmas, and the interpretability challenges posed by "black box" models, must be addressed to enable widespread adoption.
Wu et al. (Fri,) studied this question.
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