The role of saliency maps in enhancing ophthalmologists' trust in artificial intelligence models.

Journal: Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PMID: 39069106

Abstract

PURPOSE: Saliency maps (SM) allow clinicians to better understand the opaque decision-making process in artificial intelligence (AI) models by visualising the important features responsible for predictions. This ultimately improves interpretability and confidence. In this work, we review the use case for SMs, exploring their impact on clinicians' understanding and trust in AI models. We use the following ophthalmic conditions as examples: (1) glaucoma, (2) myopia, (3) age-related macular degeneration, and (4) diabetic retinopathy.

Authors

  • Carolyn Yu Tung Wong
    Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, UK.
  • Fares Antaki
    Department of Ophthalmology, University of Montreal, Montreal, QC H3T 1J4, Canada.
  • Peter Woodward-Court
    NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
  • Ariel Yuhan Ong
    Institute of Ophthalmology, University College London, London, United Kingdom.
  • Pearse A Keane
    National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.

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