Artificial intelligence in ophthalmology: Progress, challenges, and ethical implications

Abstract

The adoption of artificial intelligence (AI) in ophthalmology holds great promise for improving diagnostic accuracy, optimizing workflows, and enhancing patient care. However, regulatory, ethical, and technical challenges must be addressed to ensure its safe and effective implementation. Bias in AI can lead to disparities in healthcare delivery, while the "black-box problem" raises concerns about transparency and trust. Ethical principles must guide AI integration, particularly regarding patient safety, accountability, and liability. Privacy risks related to data collection and security are especially critical in ophthalmology, where large imaging datasets are essential. Additionally, AI-generated inaccuracies, or "hallucinations," pose potential risks to clinical decision-making. Cybersecurity threats targeting AI-powered healthcare systems further emphasize the need for robust protections. Despite these challenges, AI has the potential to improve access to ophthalmic care, particularly in underserved regions, as seen in AI-assisted diabetic retinopathy screening. However, financial and infrastructural barriers remain significant obstacles to widespread adoption. Addressing these issues requires collaboration among stakeholders, including regulators, healthcare providers, AI developers, and policymakers, to establish clear guidelines and promote trustworthy AI systems. This review explores key regulatory and ethical concerns and highlights strategies to ensure the responsible integration of AI into ophthalmology.

Keywords: AI ethical considerations; Artificial intelligence in ophthalmology; Black-box problem; Data privacy; Liability; Patient safety; Trustworthy AI.