SAFE USE OF DIGITAL MEDICAL DATA OF HIGH-TECH DIAGNOSTIC EXAMINATIONS IN EXPERT SIMULATION BASED ON VIRTUAL TWINS

Strelnykov Mykhailo; Horobets Maria; Kostenko Viktoria

doi:10.69635/mssl.2025.1.2.28

Authors

Strelnykov Mykhailo Candidate of Medical Sciences, Head of the Laboratory for the Study of Problems of Comprehensive Rehabilitation of the State Institution "Kundiev Institute of Occupational Medicine of the National Academy of Medical Sciences of Ukraine", Kyiv, Ukraine Author https://orcid.org/0009-0006-1376-6664
Horobets Maria Researcher of the Laboratory for the Study of Problems of Complex Rehabilitation, MDF of the Centre for Comprehensive Rehabilitation of the State Institution "Institute of Occupational Medicine named after Y.I. Kundiev of the National Academy of Medical Sciences of Ukraine", Kyiv, Ukraine Author https://orcid.org/0009-0008-3756-1553
Kostenko Viktoria Bachelor, Ministry of Justice of Ukraine Author https://orcid.org/0009-0006-5939-6451

DOI:

https://doi.org/10.69635/mssl.2025.1.2.28

Keywords:

Digital Twin, Magnetic Resonance Imaging, Medical Digital Data, MRI/CT Falsification, Privacy, Cybersecurity, Expert Medicine, Zero Trust, Multimodal Artificial Intelligence, GDPR, HIPAA

Abstract

The article discusses the prerequisites for the safe use of digital medical data of high-tech diagnostic examinations, primarily magnetic resonance imaging (MRI), in expert medical modelling based on virtual patient twins (Digital Twin). Based on the analysis of modern research on privacy, security, and ethics of digital twins in medicine, cybersecurity, and the metaverse, the composition of the main threats has been clarified: falsification of images and metadata, personal data leaks, and non-transparent secondary use of information. A conceptual architecture of a medical digital twin for expert and forensic purposes is proposed, which integrates cryptographically secure storages, event logs, algorithms for detecting fake MRI data, and legal information processing policies. For quantitative assessment of risks, a probabilistic model has been built, as well as an optimization setting of the choice of technical and organizational protection measures. For automated data integrity control, a combination of convolutional and recurrent neural network (CNN+LSTM) for image analysis and an autoencoder with LSTM (AE+LSTM) for monitoring metadata sequences and access logs were used. It is shown how the proposed model is consistent with the requirements of the legislation of Ukraine on personal data protection, EU Regulation 2016/679 (GDPR) and the US HIPAA act and can be implemented within the framework of the Zero Trust architecture in medical information systems. The practical result of the work is the formation of a holistic methodology for the design of medical digital twins, focused on the expert use of MRI data, which minimizes legal risks and increases the reliability of conclusions.

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