# When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis

arXiv: https://arxiv.org/abs/2403.07834 · HTML: https://mlsanity.com/paper/2403.07834/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development](https://mlsanity.com/paper/2009.07386/) (2020)
- [Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis](https://mlsanity.com/paper/2204.02976/) (2022)
- [REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays](https://mlsanity.com/paper/2109.14187/) (2021)
- [Human Attention in Fine-grained Classification](https://mlsanity.com/paper/2111.01628/) (2021)
- [Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning](https://mlsanity.com/paper/2205.12466/) (2022)
- [RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification](https://mlsanity.com/paper/2202.11781/) (2022)
- [Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing](https://mlsanity.com/paper/2003.13987/) (2020)
- [A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning](https://mlsanity.com/paper/1802.06260/) (2018)
- [Learning Better Contrastive View from Radiologist's Gaze](https://mlsanity.com/paper/2305.08826/) (2023)
- [Improving Disease Classification Performance and Explainability of Deep Learning Models in Radiology with Heatmap Generators](https://mlsanity.com/paper/2207.00157/) (2022)
- [Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotation](https://mlsanity.com/paper/2207.09771/) (2022)
- [On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks](https://mlsanity.com/paper/2202.02764/) (2022)
- [Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study](https://mlsanity.com/paper/2302.02940/) (2023)
- [Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology](https://mlsanity.com/paper/2207.14781/) (2022)
- [Discrimination of Radiologists Utilizing Eye-Tracking Technology and Machine Learning: A Case Study](https://mlsanity.com/paper/2308.02748/) (2023)

## Built on by (most cited first, 2 of 2)

- [Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images](https://mlsanity.com/paper/2504.15007/) (2025)
- [GazeXPErT: An Expert Eye-tracking Dataset for Interpretable and Explainable AI in Oncologic FDG-PET/CT Scans](https://mlsanity.com/paper/2603.00162/) (2026)

## Abstract (arXiv)

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.