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Digital twins are virtual models that mirror individual patient profiles, making it possible to customize treatments and predict health outcomes more accurately. Through a refined selection process, we have identified 17 distinct applications of this technology in the past four years, each offering significant contributions to AI-driven healthcare innovation. This review highlights the progress of AI-powered digital twins in areas such as heart health, diabetic care, mental wellness, respiratory health, and stress management. To support reader understanding and accessibility, we present intuitive visuals that break down complex processes, aiming to give a clear view of AI\u2019s expanding potential to reshape healthcare toward more proactive and patient-specific outcomes.<\/jats:p>","DOI":"10.1186\/s40537-025-01280-w","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T09:44:36Z","timestamp":1761039876000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Digital twins in healthcare: a review of AI-powered practical applications across health domains"],"prefix":"10.1186","volume":"12","author":[{"given":"Ziad","family":"Elgammal","sequence":"first","affiliation":[]},{"given":"M. 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