The article "Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions" by Tuan D. Pham, Muy-Teck Teh, Domniki Chatzopoulou, Simon Holmes, and Paul Coulthard, published in *Barts and The London School of Medicine and Dentistry*, Queen Mary University of London, reviews the advancements and applications of artificial intelligence (AI) in the field of head and neck cancer (HNC). The authors highlight how AI technologies, including deep learning and natural language processing, are enhancing diagnostic accuracy and personalizing treatment strategies. They explore the integration of AI with imaging techniques, genomics, and electronic health records, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite challenges such as data quality and algorithmic bias, emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. The review also discusses the importance of interdisciplinary collaboration among AI experts, clinicians, and researchers to develop equitable and effective AI applications. The future of AI in HNC is promising, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.The article "Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions" by Tuan D. Pham, Muy-Teck Teh, Domniki Chatzopoulou, Simon Holmes, and Paul Coulthard, published in *Barts and The London School of Medicine and Dentistry*, Queen Mary University of London, reviews the advancements and applications of artificial intelligence (AI) in the field of head and neck cancer (HNC). The authors highlight how AI technologies, including deep learning and natural language processing, are enhancing diagnostic accuracy and personalizing treatment strategies. They explore the integration of AI with imaging techniques, genomics, and electronic health records, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite challenges such as data quality and algorithmic bias, emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. The review also discusses the importance of interdisciplinary collaboration among AI experts, clinicians, and researchers to develop equitable and effective AI applications. The future of AI in HNC is promising, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.