Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images

Eye-tracking analysis plays a vital role in medical imaging, providing key insights into how radiologists visually interpret and diagnose clinical cases. In this work, we first analyze radiologists' attention and agreement by measuring the distribution of various eye-movement patterns, including saccades direction, amplitude, and their joint distribution. These metrics help uncover patterns in attention allocation and diagnostic strategies. Furthermore, we investigate whether and how doctors' gaze behavior shifts when viewing authentic (Real) versus deep-learning-generated (Fake) images. To achieve this, we examine fixation bias maps, focusing on first, last, short, and longest fixations independently, along with detailed saccades patterns, to quantify differences in gaze distribution and visual saliency between authentic and synthetic images.

@article{wong2025_2504.15007,
  title={ Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images },
  author={ David C Wong and Bin Wang and Gorkem Durak and Marouane Tliba and Mohamed Amine Kerkouri and Aladine Chetouani and Ahmet Enis Cetin and Cagdas Topel and Nicolo Gennaro and Camila Vendrami and Tugce Agirlar Trabzonlu and Amir Ali Rahsepar and Laetitia Perronne and Matthew Antalek and Onural Ozturk and Gokcan Okur and Andrew C. Gordon and Ayis Pyrros and Frank H Miller and Amir A Borhani and Hatice Savas and Eric M. Hart and Elizabeth A Krupinski and Ulas Bagci },
  journal={arXiv preprint arXiv:2504.15007},
  year={ 2025 }
}