The object of the study is a patient health monitoring system that uses service-oriented architecture (SOA) and artificial intelligence (AI) to integrate and analyze medical data. Such a system integrates data from a variety of sources, including medical devices, health apps, electronic health records, and wearables and physiological performance recorders, providing a comprehensive approach to health monitoring. Thanks to SOA, the system is able to process large arrays of data in real time, providing the opportunity to quickly process and analyze them. This allows medical professionals to get a comprehensive picture of patients' health, taking into account both long-term trends and real-time indicators. One of the most challenging areas is ensuring effective integration and processing of disparate data from various medical devices and applications for accurate diagnosis and prognosis of diseases. It is also important to create a system that is easily scalable and can be adapted to the needs of different medical facilities and various monitoring systems. As a result of the research, it is concluded that the use of SOA allows creating flexible and scalable systems capable of integrating a wide range of medical devices and applications. The use of AI in these systems makes it possible to automatically detect deviations in health indicators, recognize pathologies in the early stages and predict the possible development of diseases. This is due to the fact that the proposed architecture has a number of features, in particular, the ability to collect, process and analyze large volumes of medical data in real time. Artificial intelligence algorithms provide high accuracy of diagnosis and forecasting thanks to the ability to quickly process complex data and find hidden patterns. Thanks to this, it is possible to obtain accurate and reliable indicators of the state of health of patients. Compared to similar known systems, it provides such advantages as increased efficiency of medical care, reduced risk of complications, early detection of diseases and a personalized approach to patient treatment, as well as the concentration of all data in one system.
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Boloban et al. (2024) studied this question.