Reshapes oral cancer diagnosis from clinical imaging to precision

A narrative review published in Discover Artificial Intelligence says AI is moving beyond basic image classification in oral cancer care, mapping how machine learning and deep learning can use multiple data types to generate clinically task-specific outputs. Led by Shivam Agarwal and colleagues, the review describes processing of clinical photos, digitized and whole-slide pathology images, radiology exams, and molecular or clinical data—from flagging suspicious oral lesions to predicting patient response to radiotherapy, chemotherapy, targeted therapy, and immunotherapy. The authors argue the key is the relationship between input, computational task, and clinically relevant output, rather than model design alone. They highlight that oral squamous cell carcinoma is often diagnosed late despite the oral cavity being accessible to examination.