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

• Type: Another Publication in an International Scientific Journal
• Date: 2024

Authors

Nakayama, LF (Author) Other; Matos, J (Author) Other; Quion, J (Author) Other; Novaes, F (Author) Other; Mitchell, WG (Author) Other; Mwavu, R (Author) Other; Hung, CJYJ (Author) Other; Santiago, APD (Author) Other; Phanphruk, W (Author) Other; Jaime S Cardoso (Author) FEUP; Celi, LA (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: PLOS Digital Health

Vol. 3

ISSN: 2767-3170

Publisher: Public Library of Science

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-MX7

DOI: 10.1371/journal.pdig.0000618

Abstract (EN): Over 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14