Approaches to Sampling for Quality Control of Artificial Intelligence in Biomedical Research.

The aim of the study is to evaluate the efficacy of approaches to sampling during periodic quality control of the artificial intelligence (AI) results in biomedical practice. Materials and Methods: The approaches to sampling based on point statistical estimation, statistical hypothesis testing, employing ready-made statistical tables, as well as options of the approaches presented in GOST R ISO 2859-1-2007 "Statistical methods. Sampling procedures for inspection by attributes" have been analyzed. We have considered variants of sampling of different sizes for general populations from 1000 to 100,000 studies.The analysis of the approaches to sampling was carried out as part of an experiment on the use of innovative technologies in computer vision for the analysis of medical images and their further application in the healthcare system of Moscow (Russia). Results: Ready-made tables have specific statistical input data, which does not make them a universal option for biomedical research. Point statistical estimation helps to calculate a sample based on given statistical parameters with a certain confidence interval. This approach is promising in the case when only a type I error is important for the researcher, and a type II error is not a priority. Using the approach based on statistical hypothesis testing makes it possible to take account of type I and II errors based on the given statistical parameters. The application of GOST R ISO 2859-1-2007 for sampling allows using ready-made values depending on the given statistical parameters.When evaluating the efficacy of the studied approaches, it was found that for our purposes, the optimal number of studies during AI quality control for the analysis of medical images is 80 items. This meets the requirements of representativeness, balance of the risks to the consumer and the AI service provider, as well as optimization of labor costs of employees involved in the process of quality control of the AI results.

Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies.

The aim of the study was to develop a methodology for conducting post-registration clinical monitoring of software as a medical device based on artificial intelligence technologies (SaMD-AI). Materials and Methods: The methodology of post-registration clinical monitoring is based on the requirements of regulatory legal acts issued by the Board of the Eurasian Economic Commission. To comply with these requirements, the monitoring involves submission of the review of adverse events reports, the review of developers' routine reports on the safety and efficiency of SaMD-AI, and the assessment of the system for collecting and analyzing developers' post-registration data on the safety and efficiency of medical devices. The methodology was developed with regard to the recommendations of the International Medical Device Regulators Forum and the documents issued by the Food and Drug Administration (USA). Field-testing of this methodology was carried out using SaMD-AI designed for diagnostic imaging. Results: The post-registration monitoring of SaMD-AI consists of three key stages: collecting user feedback, technical monitoring and clinical validation. Technical monitoring involves routine evaluation of SaMD-AI output data quality to detect and remove flaws in a timely manner, and to secure the product stability. Major outcomes include an ordered list of technical flaws in SaMD-AI and their classification using evidence from diagnostic imaging studies. The application of this methodology resulted in a gradual reduction in the number of studies with flaws due to timely improvements in artificial intelligence algorithms: the number of flaws decreased to 5% in various aspects during subsequent testing. Clinical validation confirmed that SaMD-AI is capable of producing clinically meaningful outputs related to its intended use within the functionality determined by the developer. The testing procedure and the baseline testing framework were established during the field testing. Conclusion: The developed methodology will ensure the safety and efficiency of SaMD-AI taking into account its specifics as intangible medical devices. The methodology presented in this paper can be used by SaMD-AI developers to plan and carry out the post-registration clinical monitoring.