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Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning

A systematic review and taxonomy of eye gaze-driven interpretability in deep learning for radiology.

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Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning

By José Neves, Chihcheng Hsieh, Isabel Blanco Nobre, Sandra Costa Sousa, Chun Ouyang, Anderson Maciel, Andrew T. Duchowski, Joaquim JorgeEuropean Journal of Radiology
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This paper conducts a systematic literature review to investigate the use of eye-tracking data in deep-learning architectures for radiology applications. The authors analyze 60 studies and propose a taxonomy to categorize the value of eye movement in different tasks.

They also explore how eye gaze data can promote explainability in radiology.

Abstract

This paper conducts a systematic literature review to investigate the use of eye-tracking data in deep-learning architectures for radiology applications. The authors analyze 60 studies and propose a taxonomy to categorize the value of eye movement in different tasks. They also explore how eye gaze data can promote explainability in radiology.

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eye-trackingdeep-learningradiologyinterpretabilityexplainabilityAI AgentsLarge Language ModelsSemantic InteroperabilityOntology & Taxonomy
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