Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.

National health systems are experiencing a number of common global challenges: population growth, demographic ageing, the prevalence of chronic noncommunicable diseases, the constant growth of health-care costs, while increasing resource scarcity, including staffing. At the same time, the rapid development of information technologies, including the accumulation of big data, artificial intelligence, telemedicine, remote patient monitoring, and the increasing availability of high-performance mobile devices and a high-speed Internet connection create truly unique prospects for the development of digital healthcare products and services. Growth of investment from tech giants and venture capital funds is one of the main drivers of digital healthcare transformation. More and more innovative products are being offered not to enhance the effectiveness of existing processes within health systems, but to create new alternative ways to receive medical care or reduce problems in its delivery. Thus, a key customer and a user of digital healthcare products is gradually becoming not the heads of medical organizations, public health authorities or doctors, but patients themselves. In this article, we present an analysis of the existing global trends and directions of development of the digital healthcare market, form our image of the future and the most prospective scenarios and technologies for products and services in this area.

Currently, information technologies are being actively introduced in the healthcare of the Russian Federation. The share of state and municipal medical organizations that have implemented various medical information systems increased from 3.9 % in 2007 to 91 % in 2021. One of the key tasks of informatization is the introduction of electronic medical records (EMRs), which accumulate large amounts of Real-World Data (RWD). Despite the importance of EHR as a source of RWD, they have a number of shortcomings, such as the decentralized nature of database management systems, unstructured information storage, etc. The article describes the sequential processes for collecting high-quality RWD based on EHR, including the use of artificial intelligence technologies, for the purposes of scientific research, the creation of decision support systems, statistical analysis, etc. The basis of the proposed methodology is the centralized collection of information from EMR in the so-called data lakes, where as much as possible of raw data on the patient is accumulated and subsequent extraction of data from unstructured records through natural language processing (NLP) models. The proposed technology, subject to continuous improvement, will provide a correct and comprehensive solution for the skilful understanding of any text from any medical record.

The use of artificial intelligence technologies in Russian healthcare is a priority area for implementing a national strategy for the development of artificial intelligence in the country. The introduction of digital solutions based on artificial intelligence in healthcare facilities should improve the standard of living of the population and the quality of medical care, including areas of preventive examinations, diagnostics based on image analysis, prediction of disease development, selection of optimal drug dosages, reducing the threat of pandemics, and automating and increasing the accuracy of surgical interventions. Policy management and technical regulation are under development in the field of artificial intelligence in healthcare. The domestic market for relevant solutions has been created, and some products have been certified as medical devices from Roszdravnadzor (Federal Service for Surveillance in Healthcare). Various teams of scientists are conducting research. However, Russia is still behind the leading countries in the field of artificial intelligence, such as the United States and China. Investments in healthcare products based on artificial intelligence decreased significantly in 2021. The major reasons for the lag, at least in terms of market indicators, are low demand and the inability of state medical organizations to fund artificial intelligence projects. There are also other issues related to trust in the safety and effectiveness of such solutions.

The first studies and publications devoted to the use of information technologies in the health care of the USSR appeared in the 60s of the last century. Since that time, our country has formed its own scientific school and a market for a wide variety of software products intended for use in medicine and healthcare. Since 2011, a number of major federal projects in the field of healthcare informatization have been implemented, which, in general, made it possible to provide infrastructure and basic equipment, communication channels and software for the lion’s share of medical organizations, connect them into a single secure information network and ensure the exchange and accumulation of data on the work of the health care system of the Russian Federation. The article presents a description of the history of the development of these processes, as well as the current results of informatization of the healthcare industry in Russia.

Проблемы в области профилактики сердечно-сосудистых заболеваний (ССЗ), являющихся лидерами по уровню смертности от них в мире, связаны с разными способами оценки сердечно-сосудистого риска (ССР) и точностью его определения. ЦЕЛЬ ИССЛЕДОВАНИЯ Оценить правильность расчетов ССР в реальной клинической практике. МАТЕРИАЛ И МЕТОДЫ В исследовании принимали участие 252 врача из 3 регионов России: терапевты (77%), кардиологи (15%), другие специалисты (9%). Средний возраст специалистов составил 38 лет, стаж работы — 13 лет. Врачи проводили оценку ССР по заранее подготовленным клиническим примерам с использованием разработанной для исследования электронной анкеты с 19 факторами риска ССЗ. Каждый врач осуществлял анонимную оценку 30 клинических примеров по 4 тестам: SCORE, Фрамингемской шкале, PROCAM, методике из российских рекомендаций «Диагностика и коррекция нарушений липидного обмена с целью профилактики и лечения атеросклероза» VI пересмотра. РЕЗУЛЬТАТЫ Проанализированы 6144 анкеты и 24 576 оценок по применяемым шкалам. Точность оценок врачей в целом составила 54%. Наиболее точно врачи проводили оценку по Фрамингемской шкале. Полностью правильно врачи оценили только 17% анкет. Ошибки касались недооценки риска (29,5%), переоценки (39,5%) и неправильного применения шкал (31%). Кардиологи наиболее точно определяли ССР (62% для шкалы SCORE) в сравнении с терапевтами (46%). ЗАКЛЮЧЕНИЕ Выявлены значительные неточности врачебных расчетов ССР как в целом, так и по отдельным шкалам. Наиболее сложноорганизованные шкалы дают больше ошибок в их практическом применении. Наиболее точно определяли ССР кардиологи.