Recent Advances in Machine Learning for Head and Neck Cancer Care

New Developments: Recent advancements in machine learning have led to the creation of models that can predict therapy-related toxicities in head and neck cancer patients, such as dysphagia, dysphonia, xerostomia, and weight loss. These models also assist in treatment planning and provide objective assessments for posttreatment voice and swallowing issues. Additionally, machine learning is being used to screen for laryngeal cancer through voice and speech analysis.

Importance: The application of machine learning in this field is significant as it aims to enhance the evaluation and rehabilitation of voice and swallowing functions, potentially improving patient outcomes and quality of life. It also offers a new avenue for cancer screening.

Contribution to Literature: This review highlights the potential of machine learning in managing head and neck cancer, but also points out the current limitations, such as the need for external validation, transparency, reproducibility, and the development of superior predictive models. Future work should focus on training algorithms with extensive multiinstitutional data, including sociodemographic information to minimize bias, and validating these models through clinical trials to ensure their effectiveness and applicability in clinical settings.