{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T14:20:48Z","timestamp":1779114048275,"version":"3.51.4"},"reference-count":79,"publisher":"Pensoft Publishers","issue":"6","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/share-your-work\/public-domain\/cc0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5T32CA009297-29"],"award-info":[{"award-number":["5T32CA009297-29"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["FM"],"abstract":"<jats:p>This review explores how two cutting-edge technologies\u2014telemedicine and artificial intelligence (AI)\u2014are reshaping diabetes care. Diabetes remains one of healthcare\u2019s toughest challenges, demanding round-the-clock monitoring and treatments that adapt to each patient\u2019s needs. During COVID-19, telemedicine proved its worth as a vital tool for maintaining patient care and improving health outcomes. Meanwhile, AI\u2014through machine learning (ML) and deep learning (DL)\u2014brings fresh capabilities for catching diabetes early, assessing patient risk, and spotting complications like eye and nerve damage before they become serious. We examined recent research on these technologies, particularly their roles in predicting who might develop diabetes, using Natural Language Processing (NLP) to decode messy patient records, and supporting doctors through clinical decision support systems (CDSS). Our findings reveal that telemedicine works\u2014it helps patients control their blood sugar better and keeps them satisfied with their care. However, not everyone has equal access to technology, and some healthcare providers remain skeptical. AI diagnostic tools, especially for eye screening, now match human doctors in accuracy. Though merging these technologies could revolutionize personalized diabetes care, we first need to tackle real-world obstacles: ensuring fair access for all patients, protecting sensitive health data, and making different systems work together seamlessly.<\/jats:p>","DOI":"10.3897\/folmed.67.e153728","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T16:24:46Z","timestamp":1764347086000},"source":"Crossref","is-referenced-by-count":2,"title":["AI and telemedicine in management of diabetes"],"prefix":"10.3897","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8718-533X","authenticated-orcid":true,"given":"Sava","family":"Petrov","sequence":"first","affiliation":[{"name":"Medical University of Plovdiv, Plovdiv, 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