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.

利用眼球追踪技术提取医学图像中有意义的特征和洞察力，以改进医疗诊断的方法。通过整合人类认知和人工智能发展，将眼球追踪数据与机器学习和深度学习方法相结合，提高医学图像分析的准确性和效率。该系统性综述研究着眼于探索通过眼球追踪应用和方法来增强医学图像分析中的机器学习和深度学习算法。