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

arXiv: https://arxiv.org/abs/2504.15007 · HTML: https://mlsanity.com/paper/2504.15007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [RoentGen: Vision-Language Foundation Model for Chest X-ray Generation](https://mlsanity.com/paper/2211.12737/) (2022)
- [Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models](https://mlsanity.com/paper/2402.05210/) (2024)
- [Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation](https://mlsanity.com/paper/2211.03364/) (2022)
- [Assessing the ability of generative adversarial networks to learn canonical medical image statistics](https://mlsanity.com/paper/2204.12007/) (2022)
- [M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations](https://mlsanity.com/paper/2309.07973/) (2023)
- [A multi-stage GAN for multi-organ chest X-ray image generation and segmentation](https://mlsanity.com/paper/2106.05132/) (2021)
- [A domain adaptive deep learning solution for scanpath prediction of paintings](https://mlsanity.com/paper/2209.11338/) (2022)
- [Generative Adversarial Networks for Brain Images Synthesis: A Review](https://mlsanity.com/paper/2305.15421/) (2023)
- [Synthetic Medical Imaging Generation with Generative Adversarial Networks For Plain Radiographs](https://mlsanity.com/paper/2403.19107/) (2024)
- [When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis](https://mlsanity.com/paper/2403.07834/) (2024)
- [Analysis of Human Perception in Distinguishing Real and AI-Generated Faces: An Eye-Tracking Based Study](https://mlsanity.com/paper/2409.15498/) (2024)

## Built on by (most cited first, 1 of 1)

- [What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metric](https://mlsanity.com/paper/2604.08494/) (2026)

## Abstract (arXiv)

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.