  Eye-gaze tracking research offers significant promise in enhancing various
healthcare-related tasks, above all in medical image analysis and
interpretation. Eye tracking, a technology that monitors and records the
movement of the eyes, provides valuable insights into human visual attention
patterns. This technology can transform how healthcare professionals and
medical specialists engage with and analyze diagnostic images, offering a more
insightful and efficient approach to medical diagnostics. Hence, extracting
meaningful features and insights from medical images by leveraging eye-gaze
data improves our understanding of how radiologists and other medical experts
monitor, interpret, and understand images for diagnostic purposes. Eye-tracking
data, with intricate human visual attention patterns embedded, provides a
bridge to integrating artificial intelligence (AI) development and human
cognition. This integration allows novel methods to incorporate domain
knowledge into machine learning (ML) and deep learning (DL) approaches to
enhance their alignment with human-like perception and decision-making.
Moreover, extensive collections of eye-tracking data have also enabled novel
ML/DL methods to analyze human visual patterns, paving the way to a better
understanding of human vision, attention, and cognition. This systematic review
investigates eye-gaze tracking applications and methodologies for enhancing
ML/DL algorithms for medical image analysis in depth.
