Specialist for AI-based Medical Devices and IVD Certification - Classroom (exam included)

Course timeline

1. NIS2 Requirements and ISO 27001

   Lesson 01

   • Requirements for information security, cybersecurity, and data privacy protection
   • Establishing and implementing Information Security Management Systems (ISMS)
   • Understanding the NIS2 Directive, including cybersecurity risk management and incident reporting requirements
   • Developing and implementing a compliance strategy across medical devices, information security, artificial intelligence, and data protection.

2. Navigating European Regulations MDR and IVDR

   Lesson 02

   • Introduction to key concepts and terminology in artificial intelligence
   • Overview of the European regulatory landscape for AI and medical technologies
   • EU regulations for Medical Devices (MDR) and In Vitro Diagnostic Devices (IVDR)
   • The EU AI Act and its harmonised rules for placing AI systems on the European market
   • ISO 13485 requirements for quality management systems in medical device organisations
   • IEC 62304 standards for software lifecycle processes in medical device software
   • MDCG guidance documents and their practical role in regulatory compliance
   • IMDRF international guidelines supporting global harmonisation of medical device regulations
   • IG-NB structured questionnaire used by notified bodies to evaluate AI in medical devices

3. Navigating FDA Regulations in USA

   Lesson 03

   • Introduction to key concepts and terminology in artificial intelligence
   • Overview of the regulatory framework for AI in the United States
   • FDA guidance for AI-enabled medical devices
   • ISO 13485 requirements for quality management systems used in medical device development and regulatory compliance
   • IEC 62304 standards covering software lifecycle processes for medical device software

   Exercise

   • Participants are given a fictional AI-based product and determine the applicable FDA regulatory requirements.
   • Develop a checklist of required documentation, such as risk assessments, validation data, and a Predetermined Change Control Plan.

4. Mastering Risk Management

   Lesson 04

   • Introduction to risk management fundamentals and key terminology
   • Risk classification according to the EU AI Act and MDR/IVDR
   • Overview of relevant standards, including:
     • ISO 14971 for general risk management
     • AAMI/BSI TR 34971 addressing AI-specific risks
     • IEC 62304 for risk management within the software development lifecycle

   Key Challenges and Requirements

   • Data management: selection, anonymisation, quality assurance, and ensuring representative training datasets
   • Validation and verification of AI models, including robustness and generalisability
   • Managing adaptive and continuously learning AI systems
   • Ensuring human oversight, including mechanisms for human supervision and intervention

   Exercise

   • Participants develop a risk matrix for an AI-based diagnostic tool.
   • Using a fictional AI diagnostic system, they identify potential risks (such as misclassification or data bias) and evaluate them collaboratively within a structured risk matrix.

5. Post-Market Surveillance and Change Management

   Lesson 05

   • Post-market surveillance and change management in line with the EU AI Act and MDR/IVDR
   • Monitoring and evaluating risks after a product has been released to the market
   • Managing modifications to AI models, including change notifications and model retraining
   • Reporting obligations and coordination with notified bodies and regulatory authorities