Purpose to develop a procedure for registering changes, notifying users about changes made, unifying software as a medical device based on artificial intelligence technologies (SaMD-AI) changes, as well as requirements for testing and inspectionsquality control before and after making changes. Methods The main types of changes, divided into two groups-major and minor. Major changes imply a subsequent change of a SaMD-AI version to improve efficiency and safety, to change the functionality, and to ensure the processing of new data types. Minor changes imply those that SaMD-AI developers can make due to errors in the program code. Three types of SaMD-AI testings are proposed to use: functional testing, calibration testing or control, and technical testing. ResultsThe presented approaches for validation SaMD-AI changes were introduced. The unified requirements for the request for changes and forms of their submission made this procedure understandable for SaMD-AI developers, and also adjusted the workload for the Experiment experts who checked all the changes made to SaMD-AI. Conclusion This article discusses the need to control changes in the module of SaMD-AI, as innovative products influencing medical decision making. It justifies the need to control a module operation of SaMD-AI after making changes. To streamline and optimize the necessary and sufficient control procedures, a systematization of possible changes in SaMD-AI and testing methods was carried out. KeywordsArtificial intelligence • Medical software based on artificial intelligence technologies • Software as a medical device • Modifications • Changes • Validation B Ekaterina Akhmad

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The aim of the study is to develop a method for detecting areas of text with private data on medical diagnostic images using the Tesseract module and the modified Levenshtein distance.Materials and methods. For threshold filtering, the brightness of the points belonging to the text characters in the images is determined at the initial stage. The dynamic threshold is calculated from the histogram of the brightness of the pixels of the image. Next, the Tesseract module is used for primary text recognition. Based on the tag values from DICOM files, a set of strings was formed to search for them in the recognized text. A modified Levenshtein distance was used to search for these strings. A set of DICOM files of the “Dose Report” type was used to test the algorithm. The accuracy was assessed by experts marking up blocks of private information on images.Results. A tool has been developed with a set of metrics and optimal thresholds for choosing decisive rules in finding matches that allow detecting areas of text with private data on medical images. For this tool, the accuracy of localization of areas with personal data on a set of 1131 medical images was determined in comparison with expert markup, which is 99.86%.Conclusion. The tool developed within the framework of this study allows identifying personal data on digital medical images with high accuracy, which indicates the possibility of its practical application in the preparation of data sets.