Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation

A new gaze-informed transformer framework predicts radiologist expertise from 3D gaze patterns during thoracic CT interpretation, achieving an ROC-AUC of 0.91 and F1 score of 0.86 on a held-out test set of 182 CT reading sessions from five radiologists. The model, developed by researchers and available on GitHub, integrates fixation patterns via a DINOv2 backbone with learnable log-space bias and gaze-weighted pooling, outperforming adapted methods and suggesting objective, process-based expertise assessment in radiology.

arXiv:2608.23836v1 Announce Type: new Abstract: Accurate interpretation of volumetric CT requires efficient navigation of 3D image volumes and attention to diagnostically relevant regions. While eye-tracking has been widely studied in 2D medical imaging, its use for expertise assessment in CT settings remains limited. We propose a gaze-informed transformer framework for expertise classification in thoracic CT. Using a DINOv2 backbone, radiologist fixation patterns are integrated into volumetric feature learning through 1 a learnable log-space bias in self-attention and 2 gaze-weighted pooling of patch embeddings. We trained and evaluated our approach on 182 CT reading sessions from five radiologists with varying levels of experience. On a held-out test set, the model achieves an ROC-AUC of 0.91 and F1 score of 0.86, outperforming adapted methods. These findings suggest that incorporating visual search behavior into transformers may support objective, process-based expertise assessment in radiology. Code is available via https://github.com/leiluk1/GazeToSkill.