<![CDATA[The development of Artificial Intelligence (AI) has brought significant changes to the healthcare sector, particularly in improving diagnostic accuracy, optimizing treatment, and operational efficiency of medical services. Integrating intelligent algorithms with large-scale medical data allows healthcare systems to shift from conventional approaches toward more predictive, personalized, and data-driven services. This research aims to comprehensively examine the application of AI in healthcare, ranging from diagnostic support systems to optimizing patient care and treatment. The method used is a Systematic Literature Review (SLR) with a qualitative-descriptive approach to scientific articles published between 2020 and 2025, sourced from reputable databases such as Scopus, Web of Science, PubMed, and IEEE Xplore. The study results indicate that AI has been widely applied in medical image-based diagnosis, predictive analytics, personalized treatment planning, clinical workflow optimization, and robot-assisted surgery. Although AI has proven to improve the accuracy, speed, and adaptability of healthcare services, the main challenges still lie in ethical aspects, data privacy, algorithmic transparency, and infrastructure gaps between healthcare systems. This research concludes that the successful integration of AI in healthcare services is highly dependent on the implementation of a strong ethical framework, cross-disciplinary collaboration, and the development of transparent and reliable AI models. This finding is expected to serve as a reference for researchers and practitioners in developing sustainable and patient safety-oriented AI solutions.]]>
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