2024
MD&IT №1 2024
The World Health Organization records an unprecedented increase in global health care spending with a trend of further growth until 2050. Artificial intelligence (AI) technologies are considered one of the key tools for increasing the efficiency of these expenses. One of the information sources that gives an idea of the scale and intensity of extensive research, the solutions found, their concepts and global technology leaders is the world portfolio of patent documents. In this study, a patent a...
MD&IT №1 2024
Currently, mathematical analysis and three-dimensional modeling represent a new promising way to obtain additional information, which allows the researcher to virtually observe and model complex biomechanical phenomena. Issues of dynamic neck anatomy, as well as the biomechanical characteristics of its individual structures, are of significant practical and theoretical interest regarding many areas of medicine.
Aim: to develop a virtual dynamic model of the human neck and in order to reproduce ...
Aim: to develop a virtual dynamic model of the human neck and in order to reproduce ...
MD&IT №1 2024
The purpose of this study was to evaluate the radiologists’ engagement and satisfaction with artificial intelligence software as a means to support medical decision-making.
A survey of radiologists working in public healthcare facilities under the Moscow Healthcare Department was conducted in 2021 and 2022.
The survey was completed by 333 radiologists in 2021, and by 342 – in 2022. The respondents were CT, MRI, X-ray and MMG specialists of various age and clinical experience. The study found that...
A survey of radiologists working in public healthcare facilities under the Moscow Healthcare Department was conducted in 2021 and 2022.
The survey was completed by 333 radiologists in 2021, and by 342 – in 2022. The respondents were CT, MRI, X-ray and MMG specialists of various age and clinical experience. The study found that...
MD&IT №4 2023
The application of machine learning in healthcare, as one of the more general artificial intelligence technology, has shown enormous potential for improving diagnostic and treatment outcomes for various conditions. However, success of AI-based software largely depends on the availability of high-quality medical datasets and the infrastructure built to streamline its management. Creating relevant, representative and accurately labeled datasets is a complex and expensive task that requires diverse...
MD&IT №4 2023
The Unified national medical nomenclature (UNMN) has been under development since 2022 with using the Unified Medical Language System (UMLS) Metathesaurus and other sources. UNMN is a terminological system based on ontological approach and potentially applicable in Russian language medical text annotating. Currently, terms from different clinical branches are being added to UNMN utilizing both automatized and expert ways. Often in medicine abbreviations allow expressing the meaning of the concepts...
MD&IT №4 2023
Implementation of Artificial Intelligence (AI) is considered one of the most promising directions in the digital transformation of healthcare. Such systems can improve the quality of therapeutic and diagnostic processes and the efficiency of planning and managing the healthcare industry. However, the potential of AI to enhance public health indicators and improve the functioning quality of the healthcare system is inextricably linked to ethical issues arising from the specific aspects of their creation...
MD&IT №4 2023
Aim. To demonstrate the special aspects of dataset creation for neuroimaging using the example of preparing a dataset with computed tomographic images of the brain with and without signs of intracranial hemorrhage.
Methods. The creation of the dataset is based on the methodology developed by the Scientific and Practical Clinical Center for Diagnostics and Telemedicine (regulations for preparing the dataset), which is carried out in 4 stages: planning (selection of the necessary keywords for the...
Methods. The creation of the dataset is based on the methodology developed by the Scientific and Practical Clinical Center for Diagnostics and Telemedicine (regulations for preparing the dataset), which is carried out in 4 stages: planning (selection of the necessary keywords for the...