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Explore the complex regulatory landscape governing artificial intelligence and machine learning applications in medical software through this 16-minute lesson from Yale University. Examine how global regulators are addressing the dual challenge of promoting healthcare innovation while maintaining patient safety standards, particularly for self-updating algorithms and data management protocols. Analyze international regulatory frameworks from Germany, China, and the FDA, focusing on AI lifecycle management, data governance, and validation requirements. Learn to distinguish between interpretable AI and explainable AI within regulatory contexts, including GDPR's "right to an explanation" provisions. Understand the fundamental paradigm shift from code-centric to data-centric approaches in machine learning model development and regulation. Master the principles of robust testing and validation strategies for AI/ML algorithms, comparing prospective versus retrospective trial methodologies and independent evaluation processes. Gain comprehensive insights into balancing technological advancement with regulatory compliance in the rapidly evolving field of medical AI applications.
