Artificial Intelligence: Risk Management For Medical Device



What you'll learn
Understand AI-Specific Risk Management in Medical Devices
Understand Regulatory Requirements for AI-Based Medical Devices
Implement a Comprehensive Risk Management Process
Identify and Mitigate AI-Specific Risks
Ensure Post-Market Surveillance and Continuous Improvement
Requirements
Basic Understanding of Medical Devices and AI Learners should have a foundational understanding of medical devices and how artificial intelligence (AI) is used in healthcare applications. Familiarity with basic AI concepts, such as machine learning models and their applications, is helpful but not mandatory.
Familiarity with Risk Management Principles Some experience with risk management or quality assurance processes (in any industry) would be beneficial. This includes concepts like risk assessment, mitigation strategies, and compliance.
Eagerness to Learn No prior experience with ISO 14971 or AAMI/BSI TR 34971 is required. Beginners are welcome, and the course will cover all key concepts, standards, and practices needed to understand and manage risk in AI-based medical devices.
Description
This course provides in-depth training on the application of AAMI/BSI TR 34971 and ISO 14971 standards for managing risks in AI-based medical devices. Learners will explore how these globally recognized frameworks ensure safety, compliance, and quality throughout the product lifecycle. By focusing on AI-specific challenges-such as algorithm bias, model drift, and data integrity-participants will gain valuable insights into mitigating risks associated with AI technologies in healthcare.The course covers essential topics including hazard identification, risk analysis, risk control, and the evaluation of residual risks. Additionally, learners will understand how to integrate risk management practices into both the development and post-market surveillance phases of medical device deployment.Practical examples and case studies are used to illustrate how AI-driven medical devices can meet regulatory requirements while maintaining high levels of performance and safety. The course includes several templates that learners can apply to streamline the risk management process. By the end of the course, learners will have the knowledge to effectively implement risk management processes that align with ISO 14971 and AAMI/BSI TR 34971, ensuring that their AI-based devices are compliant with international standards and regulations. This course is ideal for professionals involved in product development, quality assurance, and regulatory affairs in the medical device industry, as well as AI system developers.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: What is ISO14971
Lecture 2 What is ISO14971?
Section 3: Key Definitions and Concepts in ISO 14971
Lecture 3 Key Definitions and Concepts in ISO 14971
Section 4: Competence of personnel
Lecture 4 Competence of personnel
Section 5: Regulatory requirements
Lecture 5 Regulatory requirements
Section 6: Risk Management Process
Lecture 6 Risk Management Process
Lecture 7 Risk Management Plan
Lecture 8 Risk Analysis
Lecture 9 Risk Evaluation
Lecture 10 Risk control
Lecture 11 Evaluation of overall residual risk
Section 7: Risk management review
Lecture 12 Risk management review
Section 8: Production and post-production activities
Lecture 13 Production and post-production activities
Regulatory Affairs Professionals: Individuals working in medical device regulatory compliance who need to understand the application of ISO 14971 and AAMI/BSI TR 34971 to manage AI-specific risks and ensure regulatory alignment.,Quality Assurance and Risk Management Specialists: Professionals tasked with ensuring the safety, quality, and performance of medical devices will benefit from learning how to implement a comprehensive risk management process for AI-based devices.,Medical Device Developers and Engineers: Engineers and developers involved in creating or updating AI-powered medical devices will gain critical insights into how to identify and mitigate AI-specific risks, such as algorithm bias and model drift.,Healthcare and AI Enthusiasts: Individuals with an interest in artificial intelligence and healthcare innovation who want to learn about the intersection of AI technology and medical device regulations, safety standards, and risk management.



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