{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T17:27:00Z","timestamp":1786728420343,"version":"build-2736575974"},"reference-count":28,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"5","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>Anti-vascular endothelial growth factor (anti-VEGF) therapy is the mainstay of management for diabetic macular edema (DME), but marked variability in response, high injection frequency, and cumulative treatment burden highlight the need for tools that can individualize treatment beyond protocol-driven regimens. Artificial intelligence (AI) offers a pathway toward more individualized risk stratification and prognostic support primarily by capturing statistical associations rather than biological mechanisms. Deep learning systems have achieved great accuracy in detecting diabetic retinopathy (DR) and DME. Several autonomous DR\/DME screening solutions are in clinical use. Recent advances have applied supervised machine learning, convolutional neural networks, generative adversarial networks, and ensemble methods to multimodal data from fundus images, baseline and follow-up optical coherence tomography (OCT), along with clinical and biochemical data, to classify likely responders and non-responders. These models automatically quantify and track imaging biomarkers to accurately predict central subfield thickness and vision outcomes after loading doses, and estimate future injection burden. AI-driven decision-support tools analyze vast amounts of patient data, treatment histories, and integrate multimodal data, including fundus images, OCT images, and systemic data to provide recommendations for optimal treatment and follow-up, tailored to each individual profile. The AI systems can potentially generate individualized risk and response profiles that can support decisions on initiating therapy, choosing between agents, tailoring treat-and-extend intervals, and timing switches to steroids or combination strategies. However, issues of generalizability, transparency, workflow integration, and ethical deployment need to be systematically addressed. AI-enabled decision support for patient selection and treatment response prediction is poised to become an integral component of anti-VEGF therapy.<\/jats:p>","DOI":"10.4103\/ijo.ijo_3152_25","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T16:01:25Z","timestamp":1777478485000},"page":"690-697","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["The evolving role of artificial intelligence in optimizing treatment and patient selection in diabetic macular edema"],"prefix":"10.4103","volume":"74","author":[{"given":"Dhanashree","family":"Ratra","sequence":"first","affiliation":[{"name":"Department of Vitreoretinal Diseases, Sankara Nethralaya, Chennai, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramachandran","family":"Rajalakshmi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Dr. Mohan\u2019s Diabetes 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