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FDA:医療機器アクションプラン(GMLP)としての人工知能および機械学習(AI/ML)ソフトウェアFDA: Artificial Intelligence and Machine Learning (AI/ML) Software as a Medical Device Action Plan (GMLP)
米国食品医薬品局 (FDA) は、Center for Devices and Radiological HealthのDigital Health Center of Excellenceから「Artificial Intelligence/Machine Learning (AI/ML) -Based Software as a Medical Device (SaMD) Action Plan」 を発表した。
2021年1月12日 発表された FDA AI/ML Action Planは、2019年4月に提案したディスカッションペーパー 「Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning-Based Software as a Medical Device」 に対するステークホルダーからのフィードバックを受け、次の GMLPの策定に向けての行動計画が示されています。
[Artificial Intelligence and Machine Learning (AI/ML) Software as a Medical Device Action Plan (12-Jan-2021)]
https://www.fda.gov/news-events/press-announcements/fda-releases-artificial-intelligencemachine-learning-action-plan
https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
[The AI/ML Action Plan is in response to stakeholder feedback received from the April 2019 discussion paper]
https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf
https://www.fda.gov/media/122535/download
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proposed regulatory framework presented in the AI/ML-based SaMD discussion paper, including through the issuance of a Draft Guidance on the Predetermined Change Control Plan.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.
