review · Medicina
Artificial intelligence is transforming ophthalmic care by improving diagnostic accuracy, tailoring treatments, and broadening service delivery. Machine learning and deep learning algorithms diagnose conditions such as diabetic retinopathy, age-related macular degeneration, and glaucoma with accuracy comparable to or exceeding human specialists. Beyond diagnosis, artificial intelligence analyses large datasets to predict individual treatment responses, optimising outcomes and reducing healthcare expenditure. In surgical theatres, artificial intelligence tools assist procedures such as cataract surgery to enhance precision, improve recovery times, and lower complication rates. Furthermore, teleophthalmology platforms powered by artificial intelligence expand clinical access in remote and underserved areas. Realising this potential requires addressing significant challenges around data privacy, cybersecurity, and algorithmic bias. Advancing the field depends on developing multimodal models that incorporate genetics and patient histories, alongside cross-sector collaboration to deploy systems in low-resource settings.
Eye diseases such as glaucoma and diabetic retinopathy can cause severe vision impairment if left untreated. Integrating artificial intelligence into clinical practice enables earlier detection, personalises therapy, and improves surgical accuracy. Teleophthalmology also brings expert-level eye care to remote populations, helping to reduce global healthcare disparities and prevent avoidable vision loss.
The evidence points to software applications for clinical diagnostics, surgical assistance in cataract operations, and remote teleophthalmology screening. These technologies are applied and tested, offering tools for eye clinics and regional healthcare providers. However, full-scale commercial deployment requires resolving data privacy and algorithmic bias, with future market entry in low-resource environments depending heavily on public-private partnerships.
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The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. This review provides a comprehensive overview of the current applications and future potential of AI in ophthalmology. AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. Furthermore, AI is being utilized to develop personalized treatment plans by analyzing large datasets to predict individual responses to therapies, thus optimizing patient outcomes and reducing healthcare costs. In surgical applications, AI-driven tools are enhancing the precision of procedures like cataract surgery, contributing to better recovery times and reduced complications. Additionally, AI-powered teleophthalmology services are expanding access to eye care in underserved and remote areas, addressing global disparities in healthcare availability. Despite these advancements, challenges remain, particularly concerning data privacy, security, and algorithmic bias. Ensuring robust data governance and ethical practices is crucial for the continued success of AI integration in ophthalmology. In conclusion, future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses. Also, collaborative efforts among governments, non-governmental organizations (NGOs), and technology companies are essential to deploy AI solutions effectively, especially in low-resource settings.
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DOI: 10.3390/medicina61030433
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