Artificial Intelligence and Ophthalmic Clinical Registries.

Journal: American journal of ophthalmology
Published Date: Aug 5, 2024

Abstract

PURPOSE: The recent advances in artificial intelligence (AI) represent a promising solution to increasing clinical demand and ever limited health resources. Whilst powerful, AI models require vast amounts of representative training data to output meaningful predictions in the clinical environment. Clinical registries represent a promising source of large volume real-world data which could be used to train more accurate and widely applicable AI models. This review aims to provide an overview of the current applications of AI to ophthalmic clinical registry data.

Authors

  • Luke Tran
    From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia. Electronic address: ltra2989@uni.sydney.edu.au.
  • Himal Kandel
    From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia.
  • Daliya Sari
    From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia.
  • Christopher Hy Chiu
    From the Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, (L.T., H.K., D.S., C.H.C., S.L.W.) Sydney, New South Wales, Australia.
  • Stephanie L Watson
    Save Sight Institute, Discipline of Ophthalmology, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.

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