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Regulatory Landscape for AI in Healthcare
Regulatory Landscape for AI in Healthcare
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Pdf Summary
The document outlines the regulatory framework for AI/ML-enabled medical devices within the U.S. Food and Drug Administration (FDA), emphasizing patient safety as a core aim. It notes that the Center for Devices & Radiological Health (CDRH) ensures U.S. patients access top-tier medical devices globally. As of August 7, 2024, 950 AI/ML-enabled medical devices have received FDA authorization, serving as a testament to the widespread application of AI across various medical disciplines. <br /><br />The FDA evaluates AI medical devices based on their intended use, benefits, and risks, echoing the assessment principles for traditional medical devices. Regulatory oversight varies based on risk but includes devices serving administrative, lifestyle, and clinical functions, influenced by the 21st Century Cures Act.<br /><br />The document introduces the Digital Health Policy Navigator, an interactive tool that helps stakeholders determine if regulatory oversight applies to their software based on its intended purpose. The tool brings stakeholders to one of four outcomes, aligned with FDA guidance.<br /><br />Good Machine Learning Practices (GMLP) are crucial for AI medical device development and evaluation, incorporating best practices in algorithm design, data handling, and validation to ensure safety and efficacy. These practices are supported by the FDA, MHRA, and Health Canada, all advocating for global harmonization and stakeholder engagement.<br /><br />The FDA's approach emphasizes multi-disciplinary expertise throughout the device lifecycle, good software engineering, and security practices, and ensuring that training datasets represent intended patient populations. Post-market monitoring of AI devices, such as those predicting or diagnosing sepsis, is also highlighted.<br /><br />In summary, AI/ML-enabled devices face similar regulatory inquiries as other medical devices but require adherence to GMLP. The FDA continues to develop tools and resources to support safe and effective AI medical devices.
Asset Subtitle
Shani Haugen, PhD
Keywords
AI/ML-enabled medical devices
FDA
patient safety
Center for Devices & Radiological Health
Digital Health Policy Navigator
Good Machine Learning Practices
regulatory framework
21st Century Cures Act
post-market monitoring
global harmonization
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