Artificial Intelligence in Pediatric Clinical Medicine: Applications, Innovations and Practical Implications

Abstract

Artificial intelligence (AI) is rapidly reshaping healthcare with pediatric medicine emerging as a key area for innovation. This review summarizes the recent advances and practical applications of AI in pediatric clinical practice across cardiology, oncology, critical care, neurology, and imaging. Machine-learning models have enhanced the accuracy of congenital heart disease detection and imaging interpretation. In pediatric oncology, AI has improved tumor segmentation and treatment response assessment. Critical care tools are being developed for early sepsis prediction and automated physiological monitoring, while deep learning models in neurology have achieved near-radiologist performance in brain tumors and seizure detection. Despite these promising developments, implementation in the field of pediatrics faces significant barriers, such as limited pediatric-specific datasets, variability across developmental stages, and ethical challenges in data use. Regulatory bodies are increasingly focusing on ensuring transparency, safety, and fairness in clinical AI adoption. Innovations such as federated learning, age-adapted algorithms, and integration of genomic and clinical data hold the potential to advance precision pediatrics. Achieving meaningful clinical translation will depend on strong collaboration among pediatric clinicians, data scientists, and policymakers to balance innovation with child-centered safety and equity

Ähnliche Arbeiten