• Mr. DANETI RAJU
• Dr Tammineni Raveendra

Artificial Intelligence (AI) is reshaping the landscape of optometric information services, offering transformative solutions for both clinical and academic environments. This article explores how AI technologies—such as deep learning, natural language processing, and predictive analytics—are being integrated into optometry libraries to enhance information retrieval, diagnostic support, and research accessibility. AI-driven systems enable smarter cataloguing, personalized content recommendations, and semantic search capabilities tailored to the unique needs of vision science professionals. Recent studies demonstrate the effectiveness of AI in classifying optometric literature, forecasting research trends, and supporting diagnostic precision through intelligent data modelling. These advancements not only streamline library operations but also empower clinicians, researchers, and students with faster, more accurate access to critical resources. The paper also addresses challenges such as data privacy, ethical considerations, and the need for digital literacy among library staff. Ultimately, AI is positioning optometric libraries as dynamic hubs of innovation, bridging the gap between technology and eye care education.

Smart Libraries, Artificial Intelligence (AI), Optometric Information Services, Vision Care Technology, Health Informatics, Intelligent Information Retrieval, Assistive Technologies, Personalized Healthcare, Digital Libraries, Machine Learning in Optometry, Smart Vision Systems

"Smart Libraries for Smart Vision: AI Applications in Optometric Information Services", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.e248-e258, July-2025, Available :http://www.jetir.org/papers/JETIR2507439.pdf

"Smart Libraries for Smart Vision: AI Applications in Optometric Information Services", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppe248-e258, July-2025, Available at : http://www.jetir.org/papers/JETIR2507439.pdf