Keywords: artificial intelligence, reliability, efficiency, artificial intelligence system, radiology, radiation diagnostics, monitoring
Assessment of the reliability and effectiveness of artificial intelligence systems in radiation diagnostics at the operational stage
UDC 004.8
DOI: 10.26102/2310-6018/2025.49.2.016
In the context of the active implementation of artificial intelligence (AI) technologies in healthcare, ensuring stable, controlled and high-quality operation of such systems at the operational stage is of particular relevance. Monitoring of AI systems is enshrined in law: within three years after the implementation of medical devices, including AI systems, it is necessary to provide regular reports to regulatory authorities. The aim of the study is to develop methods for assessing the reliability and effectiveness of medical artificial intelligence for radiation diagnostics. The proposed methods were tested on the data of the Moscow Experiment on the use of innovative technologies in the field of computer vision in the direction of chest radiography, collected in 2023. The developed methods take into account a set of parameters: emerging technological defects, research processing time, the degree of agreement of doctors with the analysis results and other indicators. The proposed approach can be adapted for various types of medical research and become the basis for a comprehensive assessment of AI systems as part of the monitoring of medical devices with artificial intelligence. The implementation of these methods can increase the level of trust of the medical community not only in specific AI-based solutions, but also in intelligent technologies in healthcare in general.
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Keywords: artificial intelligence, reliability, efficiency, artificial intelligence system, radiology, radiation diagnostics, monitoring
For citation: Zinchenko V.V., Vladzimirskyy A.V., Arzamasov K.M. Assessment of the reliability and effectiveness of artificial intelligence systems in radiation diagnostics at the operational stage. Modeling, Optimization and Information Technology. 2025;13(2). URL: https://moitvivt.ru/ru/journal/pdf?id=1886 DOI: 10.26102/2310-6018/2025.49.2.016 (In Russ).
Received 04.04.2025
Revised 22.04.2025
Accepted 28.04.2025