Course objective and outcome

Artificial Intelligence is rapidly entering the medical device industry, but many organizations struggle to move beyond isolated experimentation toward structured, compliant, and scalable implementation.
 
This two day course provides a practical and regulatory focused introduction to AI implementation within Medical Device Quality Management Systems. Participants will gain insight into how AI technologies can support quality, regulatory, manufacturing, and operational processes, while remaining aligned with ISO 13485, MDR/IVDR, data integrity principles, and emerging AI governance expectations such as the EU AI Act and EudraLex Volume 4 Annex 11 principles for computerized systems and electronic data management and Annex 22 Artificial Intelligence.
 
The course combines strategic governance perspectives with practical implementation examples and workshops. Participants will learn to identify valuable AI use cases, establish governance frameworks, assess risks, and integrate AI solutions into existing quality processes in a controlled, traceable manner.
 
The focus is on enabling medtech organizations to adopt AI responsibly, effectively, and compliantly not on programming AI models.

 
Content

After completing the course, participants will be able to: 

• Understand the different categories of AI technologies relevant for medical device companies
• Understand regulatory expectations related to AI in regulated environments
• Understand how AI can be integrated into ISO 13485 Quality Management Systems
• Understand how Annex 11 and Annex 22 principles apply to AI-enabled and computerized systems
• Establish governance principles for controlled AI usage within an organization
• Identify and assess relevant AI use cases across quality, regulatory, and manufacturing functions
• Understand validation principles for AI-enabled software and AI-supported processes
• Apply risk-based thinking to AI implementation and oversight
• Evaluate opportunities and limitations of generative AI and machine learning in regulated environments 

Learning outcome

Upon completion of the course, participants will be able to: 

• Explain the key regulatory and compliance considerations related to AI in medtech
• Describe how AI governance can be implemented within a Quality Management System
• Understand how Annex 11 and Annex 22 expectations influence AI governance, data integrity, supplier management, and lifecycle oversight
• Perform high-level risk assessments of AI-supported use cases
• Identify suitable AI applications within their own organization
• Contribute to establishing internal AI policies, governance structures, and approval processes
• Understand lifecycle considerations for AI-enabled solutions, including validation, monitoring, and change management
• Participate more effectively in AI implementation initiatives and cross-functional AI discussions 

Who should attend

This course is relevant for professionals working within medical device organizations which are involved in quality, compliance, operations, digitalization, or strategic implementation of AI technologies.
 
It is particularly suited for employees working within quality assurance, regulatory affairs, quality management systems, manufacturing, validation, IT, digital transformation, operational excellence, and compliance functions, as well as managers and decision-makers responsible for AI adoption and governance within regulated environments.
 
No prior programming or technical AI experience is required.
 
Participants must bring own laptop. 

Trainers

Jesper Madsen Wagner, Expertise Director
NIRAS
 
Lone Jespersen, Senior QA Specialist - Consultant
NIRAS
 
Steen Arnesen, Consultant
NIRAS