Background Information
AI & ML in Medical Devices
As of right now, there is no globally recognized standard regarding AI & ML in Medical Devices of Healthcare.
There are multiple working groups, developing standards for all industries: Foundational standards (terminology, framework); Big Data (vocabulary, reference architecture); Trustworthiness (risk, robustness, bias); Use cases & applications; AI Governance.
Development & Commercialization
Additional Information
Whether using a predictive (Waterfall; V- model) or adaptive methodology (Agile), it is imperative that the following processes and their corresponding outputs to be incorporated in the SaMD Development:
- Software development planning (SDP)
- Requirements analysis
- SaMD architectural design
- Software unit implementation and verification
- Software integration and integration testing
- SaMD verification and validation
- Problem resolution
- Configuration management
- SaMD release & maintenance
- SaMD Design Traceability
- Risk Management Activities
SaMD is still a medical device
Manufacturers of SaMD must report any correction or removal of an SaMD if action was initiated to:
- Reduce a risk to health posed by the device, or
- Remedy a violation of the FDCA caused by the device which may present a risk to health
- Report must be submitted within 10 working days of initiating the correction or removal
- FDA interprets “initiating” the correction or removal as the date a firm makes the decision to initiate a field action (62 FR 27188)
Country-Specific Information
FDA
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HealthCanada
Artificial Intelligence has been broadly defined as the science and engineering of making intelligent machines, especially intelligent computer programs (McCarthy, 2007). Artificial intelligence can use different techniques, including models based on a statistical analysis of data, expert systems that primarily rely on if-then statements, and machine learning.
Machine Learning is an artificial intelligence technique that can be used to design and train software algorithms to learn from and act on data. Software developers can use machine learning to create an algorithm that is ‘locked’ so that its function does not change, or ‘adaptive’ so its behavior can change over time based on new data.
HealthCanada
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South Korea
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Japan
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EMA
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