Purpose <p>The advent of Digital Twins DTs in healthcare signifies a paradigm shift toward precision medicine, driven by the escalating demand for bespoke healthcare solutions. DTs, serving as virtual counterparts of tangible entities, have carved a niche in healthcare, offering a platform to simulate and scrutinize individual health dynamics. This paper aims to explore how Artificial Intelligence AI-enabled DTs can enhance healthcare outcomes by providing a AI and data-driven approach to patient management.</p> Methods <p>A systematic review was conducted to gather recent advancements in AI-enhanced healthcare DTs from databases such as IEEE Xplore, PubMed, and Scopus, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA framework. The selected studies were analyzed for their focus on AI-driven DT models, extracting key information on DT frameworks, AI methodologies, and healthcare use cases.</p> Results <p>The review reveals that AI-driven DTs hold significant potential for precision medicine, notably in simulating disease progression, improving diagnostics, and enhancing personalized medical interventions. However, the field is still fragmented due to the lack of standardized definitions and frameworks, which affects scalability and interoperability.</p> Conclusions <p>AI-enhanced DTs offer transformative opportunities to transform patient care by enabling personalized, data-driven medical interventions. Addressing current challenges, such as privacy concerns, computational requirements, and standardization, will be crucial to realize the full potential of DT technology in healthcare.</p>