The article clarifies at what stages of the life cycle of artificial intelligence systems (AIS) ethical issues arise and tells about global and domestic trends in this area. The international and national experience concerning ethical issues of the AIS use in healthcare is described. The international and national strategies for the development of AI in healthcare are analyzed and their main differences from each other are identified. Special attention is paid to the national strategy for developing AI in domestic healthcare. The main conclusions are summarized, and the importance of a strong successful healthcare system based on artificial intelligence that contributes to building trust and compliance with ethical standards is emphasized.

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 inspectionsquality 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. ResultsThe 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. KeywordsArtificial intelligence • Medical software based on artificial intelligence technologies • Software as a medical device • Modifications • Changes • Validation B Ekaterina Akhmad

During the pandemic of COVID-19, computed tomography 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 MRI examinations using MR-LUND PROTOCOL: Single-shot Fast Spin Echo (SSFSE), LAVA 3D and IDEAL 3D, EPI diffusion-weighted imaging (DWI) and Fast Spin Echo (FSE) T2WI without using respiratory or any other trigger. 3 patients also had CT scan performed. In 5 (33,3%) patients, detected lesioins were visualized on T2WI and DWI simultaneously. At the same time, 4 (26.7%) patients revealed lung tissue changes only on T2WI (P(McNemar)= 0,125, OR= 0,00 (95%), kappa=0,500). Pulmonary changes on MRI were also analyzed depending on their localization. In those patients who had CT scan, the changes were comparable to MRI. MRI of the lungs can detect features of viral pneumonia and assess severity of lung damage. The method can be used to diagnose COVID-19. These data may be applicable for interpreting other studies, such as thoracic spine MRI, detecting signs of viral pneumonia of asymptomatic patients. The current study was limited by a small sample size and absence of chest CT scans of all patients.

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.