Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review

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dc.contributor.authorNakayama, Luis Filipe
dc.contributor.authorMatos, João
dc.contributor.authorQuion, Justin
dc.contributor.authorNovaes, Frederico
dc.contributor.authorMitchell, William Greig
dc.contributor.authorMwavu, Rogers
dc.contributor.authorHung, Claudia Ju-Yi Ji
dc.contributor.authorSantiago, Alvina Pauline Dy
dc.contributor.authorPhanphruk, Warachaya
dc.contributor.authorCardoso, Jaime S
dc.contributor.authorCeli, Leo Anthony
dc.date.accessioned2024-10-14T11:20:41Z
dc.date.available2024-10-14T11:20:41Z
dc.date.issued2024-10-08
dc.description.abstractOver the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps—data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration—and delves into the risks for harm at each step and strategies for mitigating them.
dc.identifier.citationNakayama, Luis Filipe, João Matos, Justin Quion, et al. 'Unmasking Biases and Navigating Pitfalls in the Ophthalmic Artificial Intelligence Lifecycle: A Narrative Review', PLOS Digital Health, vol. 3/no. 10, (2024), pp. e0000618.
dc.identifier.issnISSN 2767-3170
dc.identifier.issnEISSN 2767-3170
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/9652
dc.language.isoen
dc.publisherPublic Library of Science
dc.titleUnmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review
dc.typeArticle
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