By automating the entire process on the self-testing platform, the human factor is minimized, which makes data manipulation (to improve results) impossible. In addition, the comparison of the service's verification results with the reference data is absolutely transparent - the developer can see what metrics were used, and how the final result reflected in the report was calculated.

Anyone can take part in improving the platform and add necessary metrics to it, which will be used to evaluate the algorithm's performance for certain medical purposes (for example, for analyzing radiographs or mammograms). However, the addition of the platform will be monitored - the only metrics that have scientific justification will be included in the platform operating on the basis of the Center, - notes Nikolai Pavlov, the developer of the platform, Head of Dataset Labeling Conveyor of the Medical Informatics, Radiomics and Radiogenomics Sector, Center for Diagnostics and Telemedicine.


The creators of the platform invite developers of AI algorithms, programmers and researchers to take part in updating and improving the platform in order to develop a uniform, universal, and user-friendly tool for self-testing of artificial intelligence algorithms intended for medical purposes in the international community. At the moment, there is no such tool aimed specifically at the clinical implementation of services based on AI technologies.

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