Medical image interpretation is a complex, context-dependent process that requires nuanced reasoning and decision-making, honed through years of clinical experience. Eye gaze patterns, a rich source of implicit expert knowledge, reveal how clinicians systematically identify and interpret critical features in medical images. This project seeks to integrate these expert visual search patterns into machine learning (ML) frameworks, addressing the current gap in how ML models interpret medical images. By leveraging expert eye gaze as a privileged data source, the project aims to enhance diagnostic accuracy, improve model trustworthiness, and provide more interpretable and clinically relevant outputs. For example, understanding how a radiologist systematically examines a chest x-ray can help in designing algorithms that mimic this process, potentially improving the identification of subtle disease indicators that might be missed by less experienced readers or automated systems. By incorporating expert search patterns, ML models can become more accurate and reliable. The anticipated outcomes include improved clinical decision-making, augmented training for novice clinicians, and explainable artificial intelligence (AI) tools capable of advancing medical care and patient outcomes. This project will develop foundational methods to characterize and integrate dynamic gaze patterns into machine learning pipelines. Algorithms will be designed to capture the spatiotemporal nature of cl