Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology.

Autor: Veritti D; Department of Medicine - Ophthalmology, University of Udine, Udine, Italy. daniele.veritti@uniud.it., Rubinato L; Department of Medicine - Ophthalmology, University of Udine, Udine, Italy., Sarao V; Department of Medicine - Ophthalmology, University of Udine, Udine, Italy.; Istituto Europeo di Microchirurgia Oculare - IEMO, Udine, Italy., De Nardin A; Department of Mathematics, Informatics and Physics, University of Udine, Udine, Italy., Foresti GL; Department of Mathematics, Informatics and Physics, University of Udine, Udine, Italy., Lanzetta P; Department of Medicine - Ophthalmology, University of Udine, Udine, Italy.; Istituto Europeo di Microchirurgia Oculare - IEMO, Udine, Italy.
Jazyk: angličtina
Zdroj: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie [Graefes Arch Clin Exp Ophthalmol] 2024 Mar; Vol. 262 (3), pp. 975-982. Date of Electronic Publication: 2023 Sep 25.
DOI: 10.1007/s00417-023-06245-4
Abstrakt: Purpose: This narrative review aims to provide an overview of the dangers, controversial aspects, and implications of artificial intelligence (AI) use in ophthalmology and other medical-related fields.
Methods: We conducted a decade-long comprehensive search (January 2013-May 2023) of both academic and grey literature, focusing on the application of AI in ophthalmology and healthcare. This search included key web-based academic databases, non-traditional sources, and targeted searches of specific organizations and institutions. We reviewed and selected documents for relevance to AI, healthcare, ethics, and guidelines, aiming for a critical analysis of ethical, moral, and legal implications of AI in healthcare.
Results: Six main issues were identified, analyzed, and discussed. These include bias and clinical safety, cybersecurity, health data and AI algorithm ownership, the "black-box" problem, medical liability, and the risk of widening inequality in healthcare.
Conclusion: Solutions to address these issues include collecting high-quality data of the target population, incorporating stronger security measures, using explainable AI algorithms and ensemble methods, and making AI-based solutions accessible to everyone. With careful oversight and regulation, AI-based systems can be used to supplement physician decision-making and improve patient care and outcomes.
(© 2023. The Author(s).)
Databáze: MEDLINE
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