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

• URL: http://arxiv.org/abs/2504.15007v2
• Date: Thu, 24 Apr 2025 09:52:55 GMT
• Title: Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images
• Authors: David C Wong, Bin Wang, Gorkem Durak, Marouane Tliba, Mohamed Amine Kerkouri, Aladine Chetouani, Ahmet Enis Cetin, Cagdas Topel, Nicolo Gennaro, Camila Vendrami, Tugce Agirlar Trabzonlu, Amir Ali Rahsepar, Laetitia Perronne, Matthew Antalek, Onural Ozturk, Gokcan Okur, Andrew C. Gordon, Ayis Pyrros, Frank H Miller, Amir A Borhani, Hatice Savas, Eric M. Hart, Elizabeth A Krupinski, Ulas Bagci,
• Abstract summary: Eye-tracking analysis plays a vital role in medical imaging, providing key insights into how radiologists visually interpret and diagnose clinical cases.<n>We first analyze radiologists' attention and agreement by measuring the distribution of various eye-movement patterns, including saccades direction, amplitude, and their joint distribution.<n>We investigate whether and how doctors' gaze behavior shifts when viewing authentic (Real) versus deep-learning-generated (Fake) images.
• Score: 5.969442345531191
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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