[2310.04997] Unmasking Biases and Navigating Pitfalls in the Ophthalmic Artificial Intelligence Lifecycle: A Review
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Computer Science > Computers and Society

arXiv:2310.04997 (cs)
[Submitted on 8 Oct 2023]

Title:Unmasking Biases and Navigating Pitfalls in the Ophthalmic Artificial Intelligence Lifecycle: A Review

Authors:Luis Filipe Nakayama, João Matos, Justin Quion, Frederico Novaes, William Greig Mitchell, Rogers Mwavu, Ju-Yi Ji Hung, Alvina Pauline dy Santiago, Warachaya Phanphruk, Jaime S. Cardoso, Leo Anthony Celi
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Abstract:Over the past two 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 pre-processing 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.
Subjects: Computers and Society (cs.CY)
Cite as: arXiv:2310.04997 [cs.CY]
  (or arXiv:2310.04997v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2310.04997

Submission history

From: Luis Filipe Nakayama [view email]
[v1] Sun, 8 Oct 2023 03:49:42 UTC (571 KB)
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