AI-Driven Sustainability: Transforming Eye Care for a Greener Future

Dr. Abhishek Mandal, PhD

Scientific Advisor, OCULAR Interface

Keywords: Artificial intelligence, Eye Care, Sustainability, Greener Future


AI-Driven Sustainability: Transforming Eye Care for a Greener Future

Eye Care facilities worldwide generate environmental waste through disposable equipment, increased energy consumption, and medication waste, which increases carbon footprint and contributes to global warming.(1) However, the emergence of artificial Intelligence (AI) will promote sustainability in the industry, allowing eye care providers to leverage AI technologies to minimise carbon footprint, promote green energy, and conserve resources while maintaining excellent service delivery.(2)

Environmental Footprint of Eye Care

Eye Care facilities’ waste can be classified as sharps, infectious, pathological, pharmaceutical, or chemical waste, all posing significant environmental challenges.(3) Disposable items from each category are the largest contributors to the ever-increasing and more challenging plastic waste. Moreover, high energy consumption used to power diagnostic equipment, lighting, and climate control systems in eye care facilities causes increased emissions of greenhouse gases.

AI-Powered Green Solutions

Integrating AI into telemedicine enables Eye Care providers to perform remote diagnostics and virtual consultations, minimising in-person visits, which reduces associated carbon emissions.(4) Moreover, telemedicine and AI-driven tools, such as retinal image analysis and automated visual testing, allow eye diseases to be detected early, accurate remote assessments, and reduce the need for specialised equipment.(5) Machine learning, a sub-branch of AI, has enabled the development of automated prediction models that predict eye diseases, minimising eye care environmental impact through efficient diagnosis and treatment and reducing unnecessary tests and procedures.(6)

Artificial Intelligence can efficiently manage resources in eye care facilities by using predictive analytics to forecast demand for supplies and equipment, preventing overstocking, a significant contributor to eye care waste. Additionally, AI has revolutionised predictive maintenance of diagnostic devices and eye care equipment by reducing frequent replacements and minimising their environmental impact. AI-powered systems can also optimise energy in eye care facilities by performing automatic lighting adjustments, climate control, and equipment operation, bringing carbon footprint to a much lower level.(7)

AI has brought and will continue to revolutionise the eye care industry; however, data privacy and safety, ethics, potential bias, and patient safety must be addressed to ensure sustainable AI use in eye care for a greener future.(8) Moreover, AI technologies and infrastructure are expensive, especially for smaller eye care facilities.

The eye care industry can embrace AI-driven sustainability and transform its practices for a greener future by leveraging AI technologies, such as telemedicine, predictive analytics, predictive maintenance, and machine learning, to reduce the carbon footprint while providing excellent patient care. The time to act is now, and by utilising AI technologies, we can pave the way for a more sustainable and eco-friendly eye care industry.

Take the first step towards a greener future in eye care. Explore AI-driven sustainability solutions, work with technology companies, and be part of the movement to ensure the eye care industry is more environmentally conscious. Together, we can transform eye care and make the world safer for everyone.



  1. Palmer, D. J., Robin, A. L., McCabe, C. M., Chang, D. F., & Ophthalmic Instrument Cleaning and Sterilization Task Force (2022). Reducing topical drug waste in ophthalmic surgery: multisociety position paper. Journal of cataract and refractive surgery48(9), 1073–1077.
  2. He, M., Li, Z., Liu, C., Shi, D., & Tan, Z. (2020). Deployment of Artificial Intelligence in Real-World Practice: Opportunity and challenge. The Asia-Pacific Journal of Ophthalmology, 9(4), 299–307.
  3. Leck A. (2021). Safe management of ophthalmic health care waste. Community eye health34(111), 15–16.
  4. Massie, J., Block, S., & Morjaria, P. (2022). The role of optometry in the delivery of eye care via Telehealth: A systematic literature review. Telemedicine Journal and E-health, 28(12), 1753–1763.
  5. Kiburg, K., Turner, A., & He, M. (2022). Telemedicine and delivery of ophthalmic care in rural and remote communities: Drawing from Australian experience. Clinical and Experimental Ophthalmology, 50(7), 793–800.
  6. Ong, J., Selvam, A., & Chhablani, J. (2021). Artificial intelligence in ophthalmology: Optimization of machine learning for ophthalmic care and research. Clinical & experimental ophthalmology, 49(5), 413–415.
  7. Zheng, J., Cai, Y., Shen, X., Zheng, Z., & Yang, W. (2015). Green energy optimization in energy harvesting wireless sensor networks. IEEE Communications Magazine, 53(11), 150–157.
  8. Lim, J. S., Hong, M., Lam, W. S. T., Zhang, Z., Teo, Z. L., Liu, Y., Ng, W. Y., Foo, L. L., & Ting, D. S. W. (2022). Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Current opinion in ophthalmology33(3), 174–187.

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