AI & Machine Learning in the Diagnosis and Care of Head and Neck Cancers

Head and neck cancers represent a biologically heterogeneous group of malignancies requiring accurate diagnosis, staging, and risk stratification for optimal management. Histopathology remains the gold standard for definitive diagnosis, while radiology plays a critical role in tumor localization, staging, and treatment planning. Traditionally, these diagnostic modalities are interpreted independently, which may limit comprehensive tumor characterization. Recent advances in artificial intelligence and machine learning, alongside the rapid adoption of digital pathology and advanced imaging technologies, have created opportunities for integrated analysis of histopathological and radiological data. Multimodal AI approaches can capture complementary microscopic and macroscopic tumor features, enabling deeper insights into tumor biology, behaviour, and clinical outcomes, with increasing relevance for personalized treatment planning and patient care. Despite growing interest, the clinical application of integrated histopathology–radiology AI models in head and neck oncology remains limited and fragmented, highlighting the need for focused investigation and consolidation of current advances. The goal of this research topic is to demonstrate how artificial intelligence and machine learning can support improved diagnosis and clinical care of patients with head and neck cancers. The focus is on practical AI applications that aid early detection, diagnostic accuracy, and clinical decision-making, including visual screening of clinical and endoscopic images, digital histopathology for identifying subtle tissue changes, and multimodal systems that integrate imaging, clinical, and genomic data. The scope also includes the use of AI in treatment planning, such as supporting radiation therapy planning, improving surgical precision, and predicting patient response to immunotherapy. In addition, AI-based approaches for outcome prediction, recurrence risk assessment, and post-treatment monitoring using digital health technologies are considered, along with aspects related to data quality, model validation, and integration into routine clinical workflows to support responsible use in clinical practice.This Research Topic focuses on the use of artificial intelligence and machine learning in the diagnosis and care of head and neck cancers, covering the following areas:• AI-based visual screening tools for early detection of head and neck lesions• Machine learning analysis of clinical photographs and endoscopic images for diagnostic support• Digital histopathology and automated interpretation of whole-slide tissue images.• Machine learning models for predicting treatment response, prognosis, and recurrence risk• AI support in radiation therapy planning and surgical decision-making• Post-treatment monitoring and surveillance using digital and mobile health technologies• Explainable and trustworthy AI approaches to improve transparency and clinician confidence• Emerging trends such as foundation models, AI-driven drug discovery, and challenges related to data bias, validation, ethics, and clinical implementation Topic editors: Nguyen Quoc Khanh Le, Arpita Rai, Deborah Sybil, Christian Simon Source: https://www.frontiersin.org/research-topics/77813/ai-machine-learning-in-the-diagnosis-and-care-of-head-and-neck-cancers