<![CDATA[The increasing prevalence of chronic diseases such as diabetes mellitus, cardiovascular disease, chronic kidney disease, and respiratory disorders has become a major challenge for healthcare systems worldwide. Delayed diagnosis, limitations in continuous patient monitoring, and the high workload of healthcare professionals have encouraged the adoption of digital technologies in medical services. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) offers a novel approach that enables real-time health data collection, machine learning-based predictive analysis, and faster and more accurate clinical decision-making. This article aims to analyze the developments, benefits, challenges, and prospects of integrating AI and IoMT to improve the accuracy of chronic disease diagnosis through a narrative literature review approach. The literature was obtained from reputable international scientific publications published between 2022 and 2026 and was analyzed using a descriptive-critical approach. The review findings indicate that AI-IoMT integration can improve diagnostic sensitivity and specificity, accelerate the identification of risk factors, support continuous patient monitoring, and facilitate the implementation of precision medicine. Various algorithms, including deep learning, convolutional neural networks, recurrent neural networks, transformers, and generative AI, have demonstrated improved capabilities in analyzing clinical data obtained from IoMT sensors, electronic health records, and wearable devices. Nevertheless, the implementation of these technologies still faces challenges related to system interoperability, cybersecurity, patient data privacy, ethical considerations, and digital infrastructure readiness. Therefore, the development of interoperability standards, data protection regulations, and multidisciplinary collaboration is essential to ensure that AI and IoMT integration can be optimally implemented within modern healthcare systems. AI-IoMT technology is projected to become a key foundation for transforming chronic disease diagnosis toward more precise, proactive, and patient-centered healthcare.]]>
