Head and neck cancer (HNC) is one of the most common malignancies worldwide, with high morbidity and mortality rates. Accurate and early recognition of HNC is crucial for improving patient prognosis and guiding personalized treatment. In recent years, artificial intelligence (AI), particularly deep learning, has demonstrated strong performance in medical image analysis, enabling automated cancer detection and classification from pathological and radiological data. However, most existing AI models operate as black boxes, providing limited transparency in their decision-making processes. This lack of interpretability restricts clinical trust, limits model validation, and hinders real-world deployment in oncology practice. Explainable Artificial Intelligence (XAI) aims to address these challenges by revealing how and why AI systems generate specific predictions. By integrating explainability with cancer recognition models, XAI can help clinicians understand critical tumor-related features, verify model reliability, and support more informed clinical decision-making. Therefore, developing explainable AI approaches for head and neck cancer recognition is essential for advancing safe, trustworthy, and clinically applicable intelligent diagnostic systems.

Despite the impressive performance of deep learning models in head and neck cancer recognition, their lack of interpretability remains a major barrier to clinical adoption. Current models often function as black boxes and struggle with data heterogeneity, label uncertainty, and domain shifts across institutions. These issues limit model transparency, reliability, and clinician trust. Recent advances in Explainable Artificial Intelligence (XAI), including attention visualization, concept-based explanations, uncertainty modeling, and multimodal learning, provide promising directions to address these challenges. Integrating explainability with robust learning strategies can enhance model generalization, support clinical validation, and facilitate the safe deployment of AI-assisted diagnostic systems for head and neck cancer recognition.

This Special Issue focuses on recent advances in Explainable Artificial Intelligence (XAI) for Head and Neck Cancer Recognition, aiming to promote transparent, reliable, and clinically applicable intelligent diagnostic systems. We welcome original research articles, reviews, and methodological studies covering, but not limited to, the following topics:

• Novel and existing interpretability methods, including saliency analysis, attention mechanisms, concept-based models, and causal explanation approaches for cancer recognition tasks.
• AI-driven analysis of histopathology whole-slide images, radiological imaging (CT, MRI, PET), endoscopic images, and digital pathology data.
• Integration of imaging, clinical, genomic, and pathological data to enhance diagnostic performance and explanation robustness.
• Multiple instance learning, semi-supervised learning, self-supervised learning, and learning under noisy or incomplete annotations.
• Research on uncertainty quantification, domain generalization, bias reduction, and reliability assessment in clinical environments.
• Clinically Meaningful Explanations
• Evaluation and Validation of Explainability
• Clinical Translation and Human–AI Collaboration

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Explainable Artificial Intelligence for Head and Neck Cancer Recognition