Articles with tag: «artificial intelligence»
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2022 № 2 Issues of healthcare informatization
The proposed material presents an analysis of changes in the requirements for a unified state information system in the field of healthcare in connection with the adoption of a new decree of the Government of the Russian Federation regulating this area. The analysis of the causes and consequences of the most significant changes in the regulation of health informatization is carried out.
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2023 № 4 Medical datasets for machine learning: fundamental principles of standartization and systematization.
Backgraund: Active implementation of artificial intelligence technologies in the healthcare in recent years promotes increasing amount of medical data for the development of machine learning models, including radiology and instrumental diagnostics data. To solve various problems of digital medical technologies, new datasets are being created through machine learning algorithms, therefore, the problems of their systematization and standardization, storage, access, rational and safe use become actual.
A i m : development of an approach to systematization and standardization of information about datasets to represent, store, apply and optimize the use of datasets and ensure the safety and transparency of the development and testing of medical devices using artificial intelligence.
M a t e r i a l s a n d m e t h o d s : analysis of own and international experience in the creation and use of medical datasets, medical reference books searching and analysis, registry structure development and justification, scientific publications search with the keywords “datasets”, “registry of medical data”, placed in the databases of the RSCI, Scopus, Web of Science.
R e s u l t s . The register of medical instrumental diagnostics datasets structure has been developed in accordance with stages of datasets lifecycle: 7 parameters at the initiation stage, 8 – at the planning stage, 70 – dataset card, 1 – version change, 14 – at the use stage, total – 100 parameters. We propose datasets classification according to the purpose of their creation, a classification of data verification methods, as well as the principles of forming names for standardization and datasets presentation clarity. In addition, the main features of the organization of maintaining this registry are highlighted: management, data quality, confidentiality and security.
C o n c l u s i o n s . For the first time, an original technology of medical datasets for instrumental diagnostics structuring and systematization is proposed. It is based on the developed terminology and principles of information classification. This makes it possible to standardize the structure of information about datasets for machine learning, and ensures the storage centralization. It also allows to get quick access to all information about the dataset, and ensure transparency, reliability and reproducibility of artificial intelligence developments. Creating a registry makes it possible to quickly form visual data libraries. This allows a wide range of researchers, developers and companies to choose data sets for their tasks. This approach ensures their widespread use, resource optimization and contributes to the rapid
development and implementation of artificial intelligence. -
2023 № 4 Medical datasets for machine learning: fundamental principles of standartization and systematization.
Backgraund: Active implementation of artificial intelligence technologies in the healthcare in recent years promotes increasing amount of medical data for the development of machine learning models, including radiology and instrumental diagnostics data. To solve various problems of digital medical technologies, new datasets are being created through machine learning algorithms, therefore, the problems of their systematization and standardization, storage, access, rational and safe use become actual.
A i m : development of an approach to systematization and standardization of information about datasets to represent, store, apply and optimize the use of datasets and ensure the safety and transparency of the development and testing of medical devices using artificial intelligence.
M a t e r i a l s a n d m e t h o d s : analysis of own and international experience in the creation and use of medical datasets, medical reference books searching and analysis, registry structure development and justification, scientific publications search with the keywords “datasets”, “registry of medical data”, placed in the databases of the RSCI, Scopus, Web of Science.
R e s u l t s . The register of medical instrumental diagnostics datasets structure has been developed in accordance with stages of datasets lifecycle: 7 parameters at the initiation stage, 8 – at the planning stage, 70 – dataset card, 1 – version change, 14 – at the use stage, total – 100 parameters. We propose datasets classification according to the purpose of their creation, a classification of data verification methods, as well as the principles of forming names for standardization and datasets presentation clarity. In addition, the main features of the organization of maintaining this registry are highlighted: management, data quality, confidentiality and security.
C o n c l u s i o n s . For the first time, an original technology of medical datasets for instrumental diagnostics structuring and systematization is proposed. It is based on the developed terminology and principles of information classification. This makes it possible to standardize the structure of information about datasets for machine learning, and ensures the storage centralization. It also allows to get quick access to all information about the dataset, and ensure transparency, reliability and reproducibility of artificial intelligence developments. Creating a registry makes it possible to quickly form visual data libraries. This allows a wide range of researchers, developers and companies to choose data sets for their tasks. This approach ensures their widespread use, resource optimization and contributes to the rapid
development and implementation of artificial intelligence. -
2023 № 6 Experience in the application of artificial intelligence technologies for the development of preventive health care on the example of the Kirov region.
Healthcare is one of the priority sectors for the practical application of artificial intelligence (AI) systems. In 2018, in the Kirov region, it was decided to launch its own regional project for the implementation of AI technologies in order to gain practical experience and understand the features, advantages and barriers to the use of AI. Improvement of preventive medicine was chosen as a priority direction.
The Russian predictive analytics platform Webiomed was chosen as the base software product. The project implementation included 3 stages: pilot testing in 2019–2020, commercial operation in the “second opinion” mode in 2021–2022. and implementation in the digital assistant mode, launched in 2023. As a result of the implementation of the 1st and 2nd stages of the project, it was possible to prove that the main advantage of AI in the analysis of big medical data is the autonomous and high accuracy of interpretation of the information available in it. An AI system for independently extracting the data necessary for analysis from electronic medical records, comparing them with data from past periods, assessing the dynamics of changes in health indicators, and identifying the emergence of dangerous trends and risk factors. Together, this allows the formation of the so-called “digital profiles” of patients, which in turn are a valuable resource for supporting management and clinical decision-making. -
2023 № 7 The use of artificial intelligence technologies as a way to ensure the quality of chest radiography.
When performing radiographic studies, errors may occur that reduce the diagnostic value of the radiographs and complicate their interpretation by radiologists and diagnostic software based on artificial intelligence technology. The creation of automated quality assessment systems will optimize this process, especially in conditions of increased workload of medical personnel.
Purpose: development of an automated quality control tool for chest radiographs, which allows for quality control of the patient’s positioning and the correctness of filling in meta-information about the study.
Material and methods. To train and test automated quality control models, were used 61505 chest radiographs, obtained from open datasets and the Unified Radiological Information Service of the Unified Medical Information Analysis System of the City of Moscow. To create models we used transfer training of deep neural network architectures VGG19 and ResNet152V2.
Results. 7 models were created: a model for determining the anatomical area of study, a model for determining projection, a model for determining photometric interpretation, models for determining incomplete visualization of the anatomical area on the frontal and lateral projections of the chest radiographs, a model for determining rotation on the lateral projection of the chest radiographs. All created models have diagnostic accuracy metrics above 95%, which allows them to be used in clinical practice. Based on the developed models, a web-based quality control tool of the chest radiographs was created, which allows analyzing the quality of X-ray datasets.
Conclusion. The active use of this quality control tool will optimize the process of assessing the quality of diagnostic studies and facilitate the processes of classification of studies and the formation of datasets. Also, this tool can be used to support the decision-making of an X-ray technician and assess the quality of the study before sending the study for processing to artificial intelligence-based services. -
2021 № 8 Evolution of information systems
In medical organizations, information systems solve the following tasks: the ability to receive and store information, quick access to it and its transmission, the ability to generate various reports, the availability of specialized workplaces of medical workers, etc. Information systems are gradually evolving, turning from a tool capable of solving the simplest management tasks into a tool capable of solving the entire range of management tasks encountered in the practice of a medical organization at all levels of management. The purpose of the study is to describe the four stages of creating information systems, highlight the characteristic features of the functioning of information systems at each stage, as well as highlight the list of the main management tasks that are being developed at the selected stage and ways to solve them. The main attention was paid to the development of information systems of medical organizations working with the children’s population.
Results. The first stage of the development of information systems began with the use of computer technology in the practice of medical organizations and was reduced to the fact that programmers of both medical and third-party organizations began to develop tasks, the totality of which later became known as “creating an information system” of a medical organization. The goal of the second stage is already the transformation of information systems of medical organizations in order to describe production processes in terms of a process approach and implement them in the form of software modules. The third stage of building information systems is the stage of describing the work of a medical organization based on mathematical models in order to justify the optimal solution of the production tasks available in it. The fourth stage of building information systems is the construction of an “information system that implements the functions of an intelligent management system of a medical organization”. The authors of the article give recommendations on the formalization of information available in a medical organization for the purpose of its
effective use by artificial intelligence.
Findings. Thus, an information system is a tool for solving management problems, its development as a tool for solving management problems requires knowledge of management theory, organization theory, process approach in management, methods of multidimensional statistical analysis and modeling methods, languages and modeling methods based on neural networks.