  Predicting human gaze behavior within computer vision is integral for
developing interactive systems that can anticipate user attention, address
fundamental questions in cognitive science, and hold implications for fields
like human-computer interaction (HCI) and augmented/virtual reality (AR/VR)
systems. Despite methodologies introduced for modeling human eye gaze behavior,
applying these models to medical imaging for scanpath prediction remains
unexplored. Our proposed system aims to predict eye gaze sequences from
radiology reports and CXR images, potentially streamlining data collection and
enhancing AI systems using larger datasets. However, predicting human scanpaths
on medical images presents unique challenges due to the diverse nature of
abnormal regions. Our model predicts fixation coordinates and durations
critical for medical scanpath prediction, outperforming existing models in the
computer vision community. Utilizing a two-stage training process and large
publicly available datasets, our approach generates static heatmaps and eye
gaze videos aligned with radiology reports, facilitating comprehensive
analysis. We validate our approach by comparing its performance with
state-of-the-art methods and assessing its generalizability among different
radiologists, introducing novel strategies to model radiologists' search
patterns during CXR image diagnosis. Based on the radiologist's evaluation,
MedGaze can generate human-like gaze sequences with a high focus on relevant
regions over the CXR images. It sometimes also outperforms humans in terms of
redundancy and randomness in the scanpaths.
