Analysing and Predicting Radiologists’ Expertise Using Eye-Tracking Data: Insights for Diagnostic Decision-Making

Abstract

Radiologists’ search strategies and decision-making processes during chest X-ray diagnosis vary with their levels of expertise. Understanding these differences can inform training programmes and support the development of tools to enhance diagnostic accuracy. We hypothesize that eye-tracking data can reveal variations in expertise levels that serve as a predictor of radiologist expertise. To investigate this, we develop a database of 191 chest X-ray images with ground-truth annotations, including diagnostic decisions and eye movement patterns from 13 radiologists of varying levels of expertise. Statistical analyses reveal distinct diagnostic search patterns associated with different expertise levels. In addition, we propose a predictive framework to estimate expertise levels based on eye-tracking data. This study advances the understanding of expertise-driven differences in diagnostic search strategies and demonstrates the potential of eye-tracking data in enhancing training in clinical radiology.

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