Improving X-ray Diagnostics through Eye-Tracking and XR
This work aims to enhance diagnostic accuracy and efficiency for radiologists, though it appears incremental as it builds on existing VR and eye-tracking technologies.
The paper tackles the problem of improving X-ray diagnostics by addressing ergonomic and room condition issues that lead to errors, proposing a method that combines eye-tracking, VR, and machine learning to assist radiologists.
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.