The FDA has released an action plan to develop a regulatory framework on artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). The FDA aims to publish the draft guidance this year.
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The plan will allow manufacturers to prespecify the modifications to the AI/ML-based software as it changes over time through learning, while an Algorithm Change Protocol explains how the algorithm “learns” and changes without comprising the medical software’s safety and effectiveness.
Action Plan Keypoints:
- Good machine learning practice (GMLP);
- Use of a patient-centered approach incorporating transparency to SaMD users;
- Improving regulatory science methods for evaluating and addressing algorithmic bias to ensure algorithm robustness; and
- Real-world performance (RWP) monitoring for AI/ML software.
In summary, as part of this Action Plan, the Agency is highlighting the following intended actions and goals:
• Develop an update to the proposed regulatory framework presented in the AI/ML-based SaMD discussion paper, including through the issuance of a Draft Guidance on the Predetermined Change Control Plan.
• Strengthen FDA’s encouragement of the harmonized development of Good Machine Learning Practice (GMLP) through additional FDA participation in collaborative communities and consensus standards development efforts.
• Support a patient-centered approach by continuing to host discussions on the role of transparency to users of AI/ML-based devices. Building upon the October 2020 Patient Engagement Advisory Committee (PEAC) Meeting focused on patient trust in AI/ML technologies, hold a public workshop on medical device labeling to support transparency to users of AI/ML-based devices.
• Support regulatory science efforts on the development of methodology for the evaluation and improvement of machine learning algorithms, including for the identification and elimination of bias, and on the robustness and resilience of these algorithms to withstand changing clinical inputs and conditions.
• Advance real-world performance pilots in coordination with stakeholders and other FDA
programs, to provide additional clarity on what a real-world evidence generation program could look like for AI/ML-based SaMD.
For more details, please see the guidance from the FDA.
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