EC Ophthalmology

Artificial Intelligence is redefining the landscape of cataract and intraocular lens (IOL) surgery at a pace and scale never before witnessed in the history of ophthalmology [1-7]. For decades, cataract surgery has been celebrated as one of the safest and most frequently performed surgical interventions globally, with millions of procedures restoring sight every year. However, the definition of success in this field has shifted dramatically. It is no longer sufficient to simply remove the cloudy lens and restore visual transparency; the modern goal is refractive perfection. Patients today expect spectacle independence, high-contrast vision, and immediate functional recovery, regardless of their ocular complexity. This escalating demand for precision, coupled with an aging global population and increasing surgical volumes, has created a critical need for solutions that transcend human limitations. Artificial Intelligence (AI) has emerged not merely as a tool but as a central operating system for this new era. By integrating deep learning systems, convolutional neural networks (CNNs), and massive multimodal datasets, AI is bringing unprecedented accuracy to every stage of the patient journey-from the earliest diagnostic signals to the intricate mathematics of IOL power selection, the split-second decisions of the operating room, and the long-term management of ocular health.

1. Hill W., et al. “Advances in AI-based intraocular lens power calculations”. Journal of Cataract and Refractive Surgery2 (2024): 145-158.
2. Chang DF., et al. “Integration of AI in modern cataract surgery: current applications and future directions”. Ophthalmology11 (2023): 1234-1246.
3. Ueno Y., et al. “Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases”. British Journal of Ophthalmology10 (2024): 45-52.
4. Kane JX., et al. “Big data optimization in cataract surgery and IOL calculations”. American Journal of Ophthalmology1 (2025): 45-59.
5. Devgan U., et al. “Smart operating rooms and AI-enhanced phacoemulsification systems”. Eye World Journal3 (2024): 34-42.
6. Lundström M., et al. “Development of machine learning models to predict posterior capsule rupture based on the EUREQUO registry”. Acta Ophthalmologica 6 (2023): 644-650.
7. Ting DSW., et al. “Generative artificial intelligence in ophthalmology: current innovations, future applications, and challenges”. Asia-Pacific Journal of Ophthalmology1 (2024): 100-112.