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Changes in software as a medical device based on artificial intelligence technologies.

Victoria Zinchenko1, Sergey Chetverikov2, Ekaterina Akhmad3

  • 1The Department of Innovative Technologies, State Budget-Funded Health Care Institution of the City of Moscow "Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department", 24 Petrovka Str., Bldg. 1, 127051, Moscow, Russia.

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Summary
This summary is machine-generated.

This study introduces a standardized procedure for managing changes in AI-based software as a medical device (SaMD-AI). It outlines methods for classifying changes, user notification, and quality control testing to ensure SaMD-AI safety and efficacy.

Keywords:
Artificial intelligenceChangesMedical software based on artificial intelligence technologiesModificationsSoftware as a medical deviceValidation

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Area of Science:

  • Medical device regulation
  • Artificial intelligence in healthcare
  • Software quality assurance

Background:

  • Software as a Medical Device based on Artificial Intelligence (SaMD-AI) is rapidly evolving.
  • Ensuring the safety and efficacy of SaMD-AI requires robust change management protocols.
  • Current procedures for SaMD-AI modifications may lack standardization, impacting quality control.

Purpose of the Study:

  • To establish a comprehensive procedure for registering and notifying users of SaMD-AI changes.
  • To unify requirements for testing and quality control of SaMD-AI before and after modifications.
  • To develop a systematic approach for managing both major and minor changes in SaMD-AI.

Main Methods:

  • Categorization of SaMD-AI changes into major (affecting efficiency, safety, functionality, or data processing) and minor (code error corrections).
  • Proposal of three key testing types: functional testing, calibration/control testing, and technical testing.
  • Development of unified requirements for change requests and submission forms.

Main Results:

  • Introduction of validated approaches for SaMD-AI change assessment.
  • Standardized change request procedures improved clarity for SaMD-AI developers.
  • Optimized workload for quality control experts reviewing SaMD-AI modifications.

Conclusions:

  • Controlling changes in SaMD-AI modules is crucial due to their impact on medical decision-making.
  • Post-change operational control of SaMD-AI is essential for patient safety.
  • Systematization of SaMD-AI changes and testing methods streamlines quality control procedures.