Artificial intelligence in pediatric head and neck tumor imaging: a mini review of current applications and future directions

Artificial intelligence (AI) is creating new opportunities to enhance medical imaging in pediatric oncology through quantitative image analysis and clinical decision support. This mini review examines the emerging role of AI in pediatric head and neck tumor imaging, including applications in lesion detection and characterization, tumor segmentation and volumetric analysis, treatment planning, longitudinal surveillance, prognostication, and precision medicine. Particular attention is given to the challenges of translating these approaches to pediatric populations, where rare and heterogeneous tumors, limited datasets, variable imaging protocols, and insufficient multicenter validation remain important barriers. We also discuss emerging strategies that may facilitate clinical translation, including federated learning, multimodal data integration, explainable AI, and integration with existing radiology workflows. Although pediatric-specific evidence remains limited, continued multicenter collaboration and prospective validation may enable AI-based tools to provide more reproducible quantitative imaging assessment and increasingly personalized clinical decision support. Ultimately, the clinical value of these technologies will depend on their ability to augment radiologist expertise and improve patient care through responsible integration into routine practice.