The scope of diagnostic medical examinations increases from year to year causing a reasonable desire to develop and implement new technologies to diagnostics and medical data analysis. Artificial intelligence (AI) algorithms became one of the most promising solutions to this problem and proved themselves in the course of mass practical application. During the three-year Moscow experiment started in 2020, the possibility was achieved to develop methodologies of AI use and to successfully implement it into the regional level healthcare system.In this article, the authors share their experience in developing a medical AI service using the example of PhthisisBioMed AI service and the results of its application in real clinical activities environment. This AI service has shown its quality and reliability confirmed by technological monitoring.Clinical trials of PhthisisBioMed AI service were conducted on a specially prepared verified data set (n=1536) considering epidemiological indicators of the thoracic organs major diseases prevalence. The mean sensitivity of the service was 0.975 (95% CI: 0.966-0.984).PhthisisBioMed medical AI service is registered as a medical device (medical device registration certificate No.RZN 2022/17406 dated May 31, 2022) and is actively used in the Russian Federation as a diagnostic tool to reduce the burden on radiologists and to accelerate the process of medical report obtaining.

PURPOSE:to develop a procedure for registering changes, notifying users about changes made, unifying software as a medical device based on artificial intelligence technologies (SaMD-AI) changes, as well as requirements for testing and inspections-quality control before and after making changes.METHODS:The main types of changes, divided into two groups-major and minor. Major changes imply a subsequent change of a SaMD-AI version to improve efficiency and safety, to change the functionality, and to ensure the processing of new data types. Minor changes imply those that SaMD-AI developers can make due to errors in the program code. Three types of SaMD-AI testings are proposed to use: functional testing, calibration testing or control, and technical testing.RESULTS:The presented approaches for validation SaMD-AI changes were introduced. The unified requirements for the request for changes and forms of their submission made this procedure understandable for SaMD-AI developers, and also adjusted the workload for the Experiment experts who checked all the changes made to SaMD-AI.CONCLUSION:This article discusses the need to control changes in the module of SaMD-AI, as innovative products influencing medical decision making. It justifies the need to control a module operation of SaMD-AI after making changes. To streamline and optimize the necessary and sufficient control procedures, a systematization of possible changes in SaMD-AI and testing methods was carried out.

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.

Artificial intelligence technologies in medical practice are a promising direction in the world. Artificial intelligence medical decision support systems, diagnostic and screening programs can help medical personnel in routine and complex tasks and improve the level of medical care provided to patients. At the same time, the development, production and distribution of artificial intelligence systems must be regulated without fail. Registration and subsequent control (post-registration monitoring) of artificial intelligence systems in medicine require the creation, adjustment of the legal framework and technological regulation. The Russian Federation has developed a promising development strategy in this area. Seven national standards have been developed by experts in the field of Artificial intelligence in healthcare. These standards establish the procedures for conducting clinical and technical trials, performance requirements and the concept of life cycle, a quality management system and risk management. Aseparate standards is devoted to dataset creation for training and testing the developed algorithms, requirements for them and a metadata format. There are plans to bring the developed national standards to the international level, which will allow Russian manufacturers of artificial intelligence systems implemented these national standards to comply with foreign counterparts and become more competitive at the international level. The international community has already supported the development of an ISO standard based on the national standard for clinical trials. The development will be performed based on the technical committee ISO/TC215 (Health informatics) in conjunction with ISO/IEC JTC1/SC42 (Artificial intelligence), this will allow bringing the national requirements for the Artificial intelligence to the international level. The cycle of these standards will summarize recognized methodologies, helping both manufacturers and medical organizations, doctors and patients to produce and use aquality, safe and effective product.

During the pandemic of novel coronavirus infection (COVID-19), computed tomography (CT) showed its effectiveness in diagnosis of coronavirus infection. However, ionizing radiation during CT studies causes concern for patients who require dynamic observation, as well as for examination of children and young people. For this retrospective study, we included 15 suspected for COVID-19 patients who were hospitalized in April 2020, Russia. There were 4 adults with positive polymerase chain reaction (PCR) test for COVID-19. All patients underwent magnetic resonance imaging (MRI) examinations using MR-LUND PROTOCOL: Single-shot Fast Spin Echo (SSFSE), LAVA 3D and IDEAL 3D, Echo-planar imaging (EPI) diffusion-weighted imaging (DWI) and Fast Spin Echo (FSE) T2 weighted imaging (T2WI). On T2WI changes were identified in 9 (60,0%) patients, on DWI - in 5 (33,3%) patients. In 5 (33,3%) patients lesions of the parenchyma were visualized on T2WI and DWI simultaneously. At the same time, 4 (26.7%) patients had changes in lung tissue only on T2WI. (P(McNemar) = 0,125; OR = 0,00 (95%); kappa = 0,500). In those patients who had CT scan, the changes were comparable to MRI. The results showed that in case of CT is not available, it is advisable to conduct a chest MRI for patients with suspected or confirmed COVID-19. Considering that T2WI is a fluid-sensitive sequence, if imaging for the lung infiltration is required, we can recommend the abbreviated MRI protocol consisting of T2 and T1 WI. These data may be applicable for interpreting other studies, such as thoracic spine MRI, detecting signs of viral pneumonia of asymptomatic patients. MRI can detect features of viral pneumonia.