# Improving X-ray Diagnostics through Eye-Tracking and XR

arXiv: https://arxiv.org/abs/2203.01643 · HTML: https://mlsanity.com/paper/2203.01643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training](https://mlsanity.com/paper/2105.11333/) (2021)
- [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)
- [REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays](https://mlsanity.com/paper/2109.14187/) (2021)
- [A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning](https://mlsanity.com/paper/1802.06260/) (2018)
- [Controlling camera movement in VR colonography](https://mlsanity.com/paper/2201.02795/) (2022)

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

- [Integrating Eye-Gaze Data into CXR DL Approaches: A Preliminary study](https://mlsanity.com/paper/2302.02940/) (2023)

## Abstract (arXiv)

There is a growing need to assist radiologists in performing X-ray readings and diagnoses fast, comfortably, and effectively. As radiologists strive to maximize productivity, it is essential to consider the impact of reading rooms in interpreting complex examinations and ensure that higher volume and reporting speeds do not compromise patient outcomes. Virtual Reality (VR) is a disruptive technology for clinical practice in assessing X-ray images. We argue that conjugating eye-tracking with VR devices and Machine Learning may overcome obstacles posed by inadequate ergonomic postures and poor room conditions that often cause erroneous diagnostics when professionals examine digital images.