Artificial Intelligence in Head and Neck Cancer: An Umbrella Review

Head and neck cancers (HNCs) present significant challenges in diagnosis, treatment planning, and prognostication due to their heterogeneous nature and anatomical complexity. Artificial intelligence (AI), particularly convolutional neural networks (CNNs), has emerged as a transformative tool for developing clinical decision support systems (CDSS) that enhance precision in oncology. However, the breadth of AI applications in HNC has often obscured specific insights into their clinical impact. This umbrella review critically evaluates the role of AI-supported CDSS, with a focus on CNN-based models, in improving diagnostic accuracy, guiding treatment decisions, and refining prognostic predictions in HNC. Systematic reviews (SRs) published on AI applications in HNC were identified and synthesized. Data were extracted on clinical utility, methodological rigor, and reported limitations. The methodological quality of the included reviews was assessed using A Measurement Tool to Assess Systematic Reviews-2 (AMSTAR-2) to ensure reliability of the synthesized evidence. A total of 47 SRs met the inclusion criteria. CNN-driven CDSS demonstrated strong performance in diagnostic imaging and histopathology, with accuracy often comparable to or surpassing that of expert clinicians. In treatment planning, AI-assisted models improved tumor delineation, predicted radiotherapy-related toxicities, and provided intraoperative decision support through modalities such as hyperspectral imaging (HSI) and optical coherence tomography. Prognostic CDSS integrating radiomics, clinical, and molecular data outperformed traditional staging systems in predicting recurrence and survival. Nevertheless, widespread clinical translation is limited by retrospective study designs, small and heterogeneous datasets, lack of external validation, and concerns about interpretability. This review provides precise insights into how CNN-based models can enhance clinical decision-making in HNC. These systems hold the potential to transform oncology practice by improving diagnostic reliability, optimizing therapeutic strategies, and enabling personalized prognostic assessments. Future research should prioritize prospective multi-center validation, standardized evaluation protocols, and the development of interpretable models to ensure safe and effective integration of AI-CDSS into clinical care.