Integrating Artificial Intelligence into Eye Care: Diagnostic Performance, Workflow Impact, and Ethical Guardrails (2015–2025)

Malika Abbas; Sanjana Gupta; Ramlah Akhtar; Jamshed Ali

doi:10.36948/ijfmr.2026.v08i03.78806

Integrating Artificial Intelligence into Eye Care: Diagnostic Performance, Workflow Impact, and Ethical Guardrails (2015–2025)

Author(s)	Ms. Malika Abbas, Ms. Sanjana Gupta, Ms. Ramlah Akhtar, Mr. Jamshed Ali
Country	India
Abstract	Background Artificial intelligence (AI) has evolved into a more and more practical and influential partner in the field of ophthalmology and optometry. Because these specialties rely heavily on standardized imaging and measurable clinical data, they are particularly well suited to computational analysis. Today, AI applications extend far beyond simple diagnostics and now contribute to prognosis assessment, workflow optimization, and clinical decision support. Methods The review included English-language, peer-reviewed literature published over the period from 2015 to 2025. The Literature searches were conducted using PubMed, Scopus, and Google Scholar. Studies focusing on diagnostic accuracy, clinical implementation, and AI-based models in eye care were considered eligible for inclusion. Results Deep learning systems, especially convolutional neural networks (CNNs), continue to dominate image-based AI applications in eye care, often demonstrating diagnostic performance approaching that of experienced clinicians (AUC > 0.90). AI technologies are now being applied across retinal, glaucoma, corneal, pediatric, and neuro-ophthalmic care, where they contribute to improved screening efficiency and greater diagnostic consistency. More recently, transformer-based and multimodal models have shown promise in longitudinal prediction and in combining imaging with broader clinical data. Conclusion AI has transitioned from experimental systems to clinically relevant tools. However, successful implementation requires external validation, ethical oversight, bias mitigation, and clinician supervision. AI should augment not replace clinical judgment in modern eye care.
Keywords	Artificial Intelligence 1, Ophthalmology 2, Optometry 3, Machine Learning 4, Deep Learning 5, Clinical Decision Support 6, Ethics Validation 7, Tele-Ophthalmology 8.
Field	Medical / Pharmacy
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-18
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.78806

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Integrating Artificial Intelligence into Eye Care: Diagnostic Performance, Workflow Impact, and Ethical Guardrails (2015–2025)

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