Artificial intelligence (AI) refers to a machine’s or software’s capability to simulate intelligent human behavior for problem-solving, based on analyzing vast amounts of data, using algorithms to recognize patterns and solve complex problems. As a highly image-dependent specialty, ophthalmology is uniquely positioned for AI integration, leveraging deep-learning techniques for diagnostic algorithms and predictive analytics to revolutionize practice. Despite its immense transformative potential, the rapid integration of AI into clinical practice necessitates careful ethical consideration. This scoping review explores the major ethical implications and challenges of widespread rapid AI integration in clinical ophthalmology, including accuracy, data privacy, algorithmic bias, accountability, and generalizability. It also provides insight into rapidly evolving technological measures, regulatory guidelines, and ethical frameworks proposed to minimize these challenges and to advocate a balanced approach to AI adoption. This scoping review utilized a qualitative research design to comprehensively analyze peer-reviewed literature published in English from July 2020 to July 2025. Data was collected from three reputable academic databases: PubMed, Google Scholar, and Web of Science. Adhering to PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines and predefined inclusion/exclusion criteria, multiple-level screening was performed for data extraction. Of the 446 articles identified from the initial search, screening of the title, abstract, and full text with specific keywords led to the selection of 13 articles that addressed key ethical considerations. Those articles were chosen for detailed analysis, data extraction, and synthesis. The synthesis revealed a consistent conflict between the promising performance and efficiency of these tools, especially in complex clinical situations. The urgent need to address key ethical areas—accuracy, bias, privacy, accountability, and generalizability—is highlighted in this review. Although AI holds significant promise for transforming ophthalmology, its integration into clinical ophthalmic practice is far more complex. Responsible real-world implementation of AI requires extensive collaborative efforts involving technology developers, clinicians, regulators, and policymakers to ensure that AI tools are not only effective but also safe, transparent, and equitable for use in all patients.
Rasheed et al. (2026) studied this question.