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This paper explores how artificial intelligence \u2014computer programs that can learn from data\u2014is changing the way doctors find and treat these wounds. By reviewing 68 recent studies, we looked at how these smart technologies analyze different types of medical images to help patients. Our findings show that AI can help doctors identify health risks much earlier than traditional methods. These computer tools are also excellent at measuring how a wound is healing and predicting which treatments will work best for each individual. Because AI can spot tiny patterns in images that the human eye might miss, it makes medical care more precise and consistent. In conclusion, using AI to manage diabetic foot wounds offers a powerful way to improve patient health. By helping doctors make better, data-driven decisions, this technology can lead to faster healing and reduce the risk of serious complications for people living with diabetes.<\/jats:p>","DOI":"10.1007\/s11154-026-10041-w","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T03:27:54Z","timestamp":1778642874000},"page":"811-835","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Advances in the application of artificial intelligence-driven multi-modal imaging technologies in the comprehensive diagnosis and treatment of diabetic foot ulcers"],"prefix":"10.1007","volume":"27","author":[{"given":"Lianbo","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghang","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0085-2449","authenticated-orcid":false,"given":"Haidong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"10041_CR1","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/s2213-8587(25)00299-2","volume":"14","author":"I Genitsaridi","year":"2026","unstructured":"Genitsaridi I, et al. 11th edition of the IDF Diabetes Atlas: global, regional, and national diabetes prevalence estimates for 2024 and projections for 2050. 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