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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...

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Autores principales: Zinchenko, V.V., Arzamasov, K.M., Chetverikov, S.F., Maltsev, A.V., Novik, V.P., Akhmad, E.S., Sharova, D.E., Andreychenko, A.E., Vladzymyrskyy, A.V., Morozov, S.P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Privolzhsky Research Medical University 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171062/
https://www.ncbi.nlm.nih.gov/pubmed/37181834
http://dx.doi.org/10.17691/stm2022.14.5.02
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author Zinchenko, V.V.
Arzamasov, K.M.
Chetverikov, S.F.
Maltsev, A.V.
Novik, V.P.
Akhmad, E.S.
Sharova, D.E.
Andreychenko, A.E.
Vladzymyrskyy, A.V.
Morozov, S.P.
author_facet Zinchenko, V.V.
Arzamasov, K.M.
Chetverikov, S.F.
Maltsev, A.V.
Novik, V.P.
Akhmad, E.S.
Sharova, D.E.
Andreychenko, A.E.
Vladzymyrskyy, A.V.
Morozov, S.P.
author_sort Zinchenko, V.V.
collection PubMed
description 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.
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spelling pubmed-101710622023-05-11 Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies Zinchenko, V.V. Arzamasov, K.M. Chetverikov, S.F. Maltsev, A.V. Novik, V.P. Akhmad, E.S. Sharova, D.E. Andreychenko, A.E. Vladzymyrskyy, A.V. Morozov, S.P. Sovrem Tekhnologii Med Advanced Researches 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. Privolzhsky Research Medical University 2022 2022-09-29 /pmc/articles/PMC10171062/ /pubmed/37181834 http://dx.doi.org/10.17691/stm2022.14.5.02 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Advanced Researches
Zinchenko, V.V.
Arzamasov, K.M.
Chetverikov, S.F.
Maltsev, A.V.
Novik, V.P.
Akhmad, E.S.
Sharova, D.E.
Andreychenko, A.E.
Vladzymyrskyy, A.V.
Morozov, S.P.
Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies
title Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies
title_full Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies
title_fullStr Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies
title_full_unstemmed Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies
title_short Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies
title_sort methodology for conducting post-marketing surveillance of software as a medical device based on artificial intelligence technologies
topic Advanced Researches
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171062/
https://www.ncbi.nlm.nih.gov/pubmed/37181834
http://dx.doi.org/10.17691/stm2022.14.5.02
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