" In recent years, the healthcare industry has shown increasing interest in developing inclusive and accessible artificial intelligence (AI) technologies. The objective is to ensure that AI-powered healthcare systems are accessible to everyone and inclusive of patients from diverse backgrounds. Ethical considerations such as privacy, security, and fairness also need to be taken into account during the design process. Head and Neck cancers (HNC) are classified as rare diseases, receiving less support compared to more prevalent cancers. However, HNC is a biologically complex and diverse group of rare malignancies, particularly burdensome in India. Diagnosis often occurs at advanced stages, resulting in poor prognosis and shorter lifespans. One of the major challenges after diagnosis is determining the most effective therapy for an individual tumor. Current decision-making relies on qualitative visual interpretations of radiological images, doctor-derived observations, and average statistics from clinical trials. However, advances in molecular biomarkers enable personalized cancer treatments based on specific genetic pathways. Radiological imaging remains crucial for diagnosis and decision-making, as it allows comprehensive analysis of the tumor. Despite these advancements, clinical outcomes in HNC remain unsatisfactory. Radiomics, a rapidly evolving field in oncology, uses AI-driven quantitative analysis of radiological imaging data. It converts qualitative images into numerical features for mining clinical insights using machine learning techniques. However, there are barriers to clinical adoption of radiomics in radiation oncology, including limited transparency, insufficient patient data for robust models, and a lack of randomized trials demonstrating added value. To address these limitations, our team at Christian Medical College, Vellore, India, funded by India Alliance DBT Wellcome, is working on better understanding imaging biomarkers in head and neck cancer through large-scale studies. We aim to overcome challenges and share our experiences with prospective imaging trials and multi-institutional radiomics studies. Integrating AI and quantitative imaging in healthcare has transformative potential, but addressing challenges is crucial for successful implementation and clinical impact."
