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This study introduces a gaze-informed transformer framework that integrates radiologist eye-tracking data to classify expertise in thoracic CT interpretation. By leveraging a DINOv2 backbone, the model incorporates a learnable log-space bias in self-attention and gaze-weighted pooling of patch embeddings, achieving an ROC-AUC of 0.91 and an F1 score of 0.86 on a dataset of 182 CT reading sessions. These results highlight the potential of visual search behavior as a robust indicator of expertise in radiology, surpassing traditional methods.
Eye-tracking data can predict radiologist expertise with over 90% accuracy, revealing insights into diagnostic decision-making processes.
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.