Improving X-ray Diagnostics through Eye-Tracking and XR
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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.
Key facts. Cited by 1 paper 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
5 of 38 references.
References · 5
- 2021Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Trainingcited by 96
- 2020Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Developmentcited by 56
- 2021REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rayscited by 34
- 2018A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learningcited by 14
- 2022Controlling camera movement in VR colonographycited by 1
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Citing papers per year: 2023: 1.
1 paper.