JOURNAL DESCRIPTION
The Medical Radiology and Radiation Safety journal ISSN 1024-6177 was founded in January 1956 (before December 30, 1993 it was entitled Medical Radiology, ISSN 0025-8334). In 2018, the journal received Online ISSN: 2618-9615 and was registered as an electronic online publication in Roskomnadzor on March 29, 2018. It publishes original research articles which cover questions of radiobiology, radiation medicine, radiation safety, radiation therapy, nuclear medicine and scientific reviews. In general the journal has more than 30 headings and it is of interest for specialists working in thefields of medicine¸ radiation biology, epidemiology, medical physics and technology. Since July 01, 2008 the journal has been published by State Research Center - Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency. The founder from 1956 to the present time is the Ministry of Health of the Russian Federation, and from 2008 to the present time is the Federal Medical Biological Agency.
Members of the editorial board are scientists specializing in the field of radiation biology and medicine, radiation protection, radiation epidemiology, radiation oncology, radiation diagnostics and therapy, nuclear medicine and medical physics. The editorial board consists of academicians (members of the Russian Academy of Science (RAS)), the full member of Academy of Medical Sciences of the Republic of Armenia, corresponding members of the RAS, Doctors of Medicine, professor, candidates and doctors of biological, physical mathematics and engineering sciences. The editorial board is constantly replenished by experts who work in the CIS and foreign countries.
Six issues of the journal are published per year, the volume is 13.5 conventional printed sheets, 88 printer’s sheets, 1.000 copies. The journal has an identical full-text electronic version, which, simultaneously with the printed version and color drawings, is posted on the sites of the Scientific Electronic Library (SEL) and the journal's website. The journal is distributed through the Rospechat Agency under the contract № 7407 of June 16, 2006, through individual buyers and commercial structures. The publication of articles is free.
The journal is included in the List of Russian Reviewed Scientific Journals of the Higher Attestation Commission. Since 2008 the journal has been available on the Internet and indexed in the RISC database which is placed on Web of Science. Since February 2nd, 2018, the journal "Medical Radiology and Radiation Safety" has been indexed in the SCOPUS abstract and citation database.
Brief electronic versions of the Journal have been publicly available since 2005 on the website of the Medical Radiology and Radiation Safety Journal: http://www.medradiol.ru. Since 2011, all issues of the journal as a whole are publicly available, and since 2016 - full-text versions of scientific articles. Since 2005, subscribers can purchase full versions of other articles of any issue only through the National Electronic Library. The editor of the Medical Radiology and Radiation Safety Journal in accordance with the National Electronic Library agreement has been providing the Library with all its production since 2005 until now.
The main working language of the journal is Russian, an additional language is English, which is used to write titles of articles, information about authors, annotations, key words, a list of literature.
Since 2017 the journal Medical Radiology and Radiation Safety has switched to digital identification of publications, assigning to each article the identifier of the digital object (DOI), which greatly accelerated the search for the location of the article on the Internet. In future it is planned to publish the English-language version of the journal Medical Radiology and Radiation Safety for its development. In order to obtain information about the publication activity of the journal in March 2015, a counter of readers' references to the materials posted on the site from 2005 to the present which is placed on the journal's website. During 2015 - 2016 years on average there were no more than 100-170 handlings per day. Publication of a number of articles, as well as electronic versions of profile monographs and collections in the public domain, dramatically increased the number of handlings to the journal's website to 500 - 800 per day, and the total number of visits to the site at the end of 2017 was more than 230.000.
The two-year impact factor of RISC, according to data for 2017, was 0.439, taking into account citation from all sources - 0.570, and the five-year impact factor of RISC - 0.352.
Medical Radiology and Radiation Safety. 2025. Vol. 70. № 4
DOI:10.33266/1024-6177-2025-70-4-46-54
V.G. Barchukov, A.A. Bolotov, Y.N. Zhirnov, A.S. Samoylov, S.M. Shinkarev,
I.B. Ushakov, I.K. Tesnov, A.S. Galuzin, D.A. Kudinova, V.U. Lizunov
Using Artificial Intelligence Technologies for Radiation Protection
during Decommissioning of Radiation and Nuclear Facilities
A.I. Burnazyan Federal Medical Biophysical Center, Moscow, Russia
ABSTRACT
Background: The decommissioning of radiation-hazardous facilities (RHF) is a complex process that requires compliance with legislative and regulatory requirements. Effective document management and continuous personnel training are essential for ensuring safety and regulatory adherence. Artificial Intelligence (AI) can significantly simplify and automate documentation processing and management, reducing staff workload and minimizing errors. Additionally, AI helps ensure regulatory compliance by automatically tracking changes and maintaining adherence to standards. Furthermore, AI can analyze large volumes of data, identify potential risks, and propose optimal solutions based on predictive analytics.
Purpose: To develop an AI-based service capable of supporting a comprehensive and informed dialogue on RHF decommissioning. To achieve this, we selected a natural language processing (NLP) model based on Keras. A dataset was created for training the model, consisting of five key regulatory documents on radiation safety. The documents were divided into separate contexts, with experts formulating corresponding questions and answers. In total, 429 contexts were processed, and 6,405 questions and answers were generated.
Results: The model was tested in a specially developed application similar to ChatGPT, designed to help specialists find answers to questions arising during the decommissioning process. Additionally, a dynamic knowledge base update feature was implemented, allowing for real-time adjustments to regulatory documentation changes. The developed system demonstrated high accuracy in answering questions related to regulatory aspects of decommissioning. Machine learning algorithms trained on our dataset for text processing and interpretation proved effective in recognizing and handling user queries. The system was tested in various scenarios, including internal Keras model evaluations and test questions not included in the training dataset.
Conclusion: The obtained results confirmed the potential of AI technologies in managing RHF decommissioning processes. Furthermore, tests conducted on real-world data helped identify key areas for further system improvement and functional expansion.
Keywords: Artificial intelligence, decommissioning of radiation and nuclear facilities, regulatory documents, radiation safety, Natural Language Processing (NLP), Keras
For citation: Barchukov VG, Bolotov AA, Zhirnov YN, Samoylov AS, Shinkarev SM, Ushakov IB, Tesnov IK, Galuzin AS, Kudinova DA, Lizunov VU. Using Artificial Intelligence Technologies for Radiation Protection during Decommissioning of Radiation and Nuclear Facilities. Medical Radiology and Radiation Safety. 2025;70(4):46–54. (In Russian). DOI:10.33266/1024-6177-2025-70-4-46-54
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PDF (RUS) Full-text article (in Russian)
Conflict of interest. The authors declare no conflict of interest.
Financing. The study had no sponsorship.
Contribution. Article was prepared with equal participation of the authors.
Article received: 20.03.2025. Accepted for publication: 25.04.2025.