The Role of Artificial Intelligence in Ophthalmology: Assessing Its Hidden Environmental Impact
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By
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Bita Manzouri
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August 17, 2026
Clinical Report: The Role of Artificial Intelligence in Ophthalmology
Background
The integration of AI in ophthalmology is evolving. The Royal College of Ophthalmologists has initiated guidelines for sustainable practices, emphasizing the need for environmental considerations in ophthalmic care.
Data Highlights
No numerical data provided in the source material.
Key Findings
- AI technologies in ophthalmology are rapidly advancing, improving disease identification and clinical decision-making.
- The environmental impact of AI, including water usage and electronic waste, is significant yet often overlooked.
- Training a large AI model may require approximately 700,000 liters of clean freshwater.
- By 2027, global demand for AI could necessitate up to 6.6 billion m³ of water annually.
- Current literature lacks focus on the sustainability of AI implementation specifically in ophthalmology.
- Ophthalmology generates substantial digital data, increasing the environmental footprint associated with cloud-based systems.
Clinical Implications
Healthcare professionals should be aware of the environmental implications of AI technologies in ophthalmology.
Conclusion
As AI continues to evolve in ophthalmology, addressing its environmental impact is critical for sustainable healthcare delivery.
Related Resources & Content
- Richie et al., BMJ, 2023 -- Evaluating the environmental impact of AI in healthcare is essential for planetary health
- Royal College of Ophthalmologists, 2023 -- Guidelines for sustainable practices in ophthalmology
- Patil, Clinical & Experimental Ophthalmology, 2026 -- The Hidden Carbon Cost of Digital Ophthalmology
- retinal physician — Artificial Intelligence and Augmented Intelligence in Ophthalmic Practice
- Frontiers in Ophthalmology — Artificial intelligence in ophthalmology: from innovation to clinical integration
- Eyecare Business — AI’s Impact + Influence
- optometric management — Integrating AI Into Everyday Eyecare Practice
- Artificial Intelligence and Augmented Intelligence in Ophthalmic Practice
- Artificial intelligence in ophthalmology: from innovation to clinical integration
- AI’s Impact + Influence
- 12. Retinopathy, Neuropathy, and Foot Care: Standards of Care in Diabetes—2026 | Diabetes Care | American Diabetes Association
- Clinical setting-dependent diagnostic accuracy of artificial intelligence and store-and-forward diabetic retinopathy screening: a systematic review and meta-analysis | npj Digital Medicine
- Advancing glaucoma care: What's new in the 6th edition of the European Glaucoma Society guidelines
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices - PubMed
- Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study
- Introduction - Tele-ophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease: HERMES cluster randomised trial with a diagnostic accuracy study - NCBI Bookshelf
- Evaluating the environmental impact of AI in healthcare is essential for planetary health
- L.1801 : Guidelines for assessing the environmental impact of artificial intelligence systems
- The Hidden Carbon Cost of Digital Ophthalmology: A Call for Sustainable Data Practices - Patil - 2026 - Clinical & Experimental Ophthalmology - Wiley Online Library
- Decarbonizing the healthcare supply chain strategic actions for health systems
Based on findings from:
Artificial intelligence in ophthalmology: an invisible environmental footprint
Bita Manzouri. Eye, 2026.
https://www.nature.com/articles/s41433-026-04807-4
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
