Presentation of diagnostic accuracy metrics based on classification of artificial intelligence software in radiology

Abstract

The implementation of artificial intelligence in healthcare is a key direction for technology development in Russia, aimed at improving the quality of medical services and increasing diagnostic accuracy. However, the lack of standards for presenting metrics of diagnostic accuracy of artificial intelligence-based software (AI-based software) complicates comparative analysis and selection of the most suitable software for medical organizations. Therefore, developing a detailed classification of AI-based software is an important task for ensuring safety and quality of medical care, as well as determining the interchangeability of AI-based medical devices. Purpose: This study aims to develop a clinical classification of AI-based software in radiology. Materials and Methods: To conduct the study, a comprehensive analysis of available information on AI-based software in radiology was conducted using domestic and foreign databases. In the process of analysis, key aspects were identified, including clinical applicability of AI-based software, diagnostic accuracy of medical devices using AI in radiology. Results: a clinical classification of AI-based software in the field of radiology was developed. In addition, an important observation regarding the representation of diagnostic accuracy metrics of AI-based software was identified. As a result, the proposed classification was extended and supplemented by defining the level of representation of diagnostic accuracy metrics depending on the clinical classification. Conclusion: based on the conducted research, a clinical classification of AI-based software has been developed, which provides a unified approach to the presentation of data on diagnostic accuracy by developers. This approach improves the transparency and comparability of information about different AI-based software in medical practice, thereby improving the efficiency and safety of AI-based software use in medical practice. The results of this study have the potential to be scaled to other AI applications and can be used to improve the quality regulation system for AI-enabled medical devices.

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