Introduction / Overview
Artificial Intelligence and Machine Learning are revolutionizing healthcare, but bringing AI-enabled medical devices to market demands mastery of multiple and fast-evolving regulatory frameworks.
This course provides a complete, practical roadmap to developing and validating compliant medical AI software. Learn how to bridge EU AI Act requirements, EU MDR, and FDA expectations across the entire product lifecycle—from data governance and risk controls to predetermined change control plans (PCCP) and post-market surveillance.
Learning Outcomes / Benefits
- Gain practical skills, enabling you to classify AI systems and meet EU AI Act, MDR, and FDA submission requirements.
- Implement data management and AI-specific design controls, enabling you to validate datasets, assess performance, and generate audit-ready documentation.
- Apply AI-specific risk controls to identify failure modes, mitigate bias, and protect patient safety.
- Establish compliant post-market lifecycle using FDA PCCP and post-market surveillance.
Course Content
- Introduction
- Vocabulary, Definitions
- Regulations: FDA, MDR, AI Act
- Standards
- AI Act Regulatory Requirements
- Classification – Conformity Assessment
- Economic Operator Obligations
- QMS
- Design Process
- IEC 62304 2nd Edition
- AI System Development Plan
- AI System Design Specs
- Data Management Process
- Process Steps
- Data Quality Criteria
- Bias Detection
- Risk Management Process
- AI-Specific Risks
- AI-Specific Risk Control Measures
- Cybersecurity Risks
- Verification and Validation
- AI System Verification and Performance Tests
- Dataset Requirements
- Plans and Reports
- Technical Documentation
- Instructions for Use
- Model card, Model Description
- Market Placement and Post-Market Surveillance
- EU Declaration of Conformity
- Post-Market Surveillance Plan
- AI System-Specific Objectives and Criteria
- FDA PCCP
Target Audience
Who should attend: This course is aimed to professionals working with medical devices, SaMD (Software as a Medical Device), and IVDs who are planning, designing, or qualifying AI/ML solutions, including:
- Quality Assurance (QA) and Regulatory Affairs (RA) Managers
- Software Engineers, Data Scientists, and Machine Learning Developers
- Software Project and Product Managers
- Systems Architects, V&V Engineers, and Risk Management Leads
- R&D Directors and Compliance Officers
Prerequisites
- You should have basic knowledge of medical device regulations (EU MDR and/or US FDA QSR/QMSR).
- You should have basic familiarity with software lifecycle concepts (such as IEC 62304) and medical device risk management (ISO 14971).
- It is helpful if you have experience working on medical software projects or exposure to basic data science terminology.
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