APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE IMAGING DIAGNOSIS OF HEAD AND NECK CANCERS – A LITERATURE REVIEW
DOI:
https://doi.org/10.31435/ijitss.3(51).2026.6690Keywords:
Head and Neck Neoplasms, Artificial Intelligence, Diagnostic Imaging, Radiomics, Tumor DetectionAbstract
Introduction Head and neck cancers represent a significant clinical challenge due to their complex anatomy, biological heterogeneity, and diagnostic difficulties, particularly in the early stages of the disease. The rapid development of artificial intelligence (AI) in recent years has opened up new possibilities for medical image analysis, supporting the diagnostic process and clinical decision-making.
Current State of Knowledge The aim of this study was to review the current literature on the application of artificial intelligence in the imaging diagnosis of head and neck cancers, with particular emphasis on its clinical potential, limitations, and future directions. A nonsystematic review of publications available in the PubMed, Scopus, and Web of Science databases from 2017 to 2025 was conducted. Studies investigating the use of machine learning, deep learning, and radiomics in CT, MRI, and PET/CT imaging were analyzed.The reviewed studies indicate that AI algorithms, particularly those based on convolutional neural networks, demonstrate high performance in the detection, segmentation, and classification of neoplastic lesions. The application of radiomics enables the extraction of quantitative imaging features that may support prognostic assessment and prediction of treatment response, thereby contributing to the development of personalized medicine. However, the implementation of these technologies in clinical practice faces significant challenges, including a lack of data standardization, limited model interpretability, and the need for validation in large, multicenter studies.
Conclusions In conclusion, artificial intelligence represents a promising tool in the imaging diagnosis of head and neck cancers. However, its full integration into clinical practice requires further research, methodological standardization, and the resolution of ethical and organizational challenges.
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Copyright (c) 2026 Klaudia Ostrowicz, Dawid Furtek, Aleksandra Duda, Patrycja Drelich, Kinga Głodek, Kacper Bartosik , Dominika Brzuchacz, Joanna Borkowska, Julia Ciesielska
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