Day Five – 14 June 2024Friday, 14 June 2024 - CEST (Cent Europe Summer, GMT+2)

• How do regulatory affairs teams stand to change with the increasing incorporation of AI/ML?
  • Which skills need to take the forefront when working with SaMDs and AI/ML?
  • How can AI be harnessed to enhance internal processes?
• In the next 10 years – where do we see AI/ML going?
  • Are regulatory affairs teams at risk of being overtaken by AI/ML?

• Cybersecurity in regulations
• Who owns the cybersecurity of the device or diagnostic?
  • Incorporation of cybersecurity in the entire product lifecycle
• Testing of cybersecurity
  • Identifying and overcoming weaknesses
  • Enhancing overall security

• Regulatory considerations in early development
• Access publicly available databases early on
• “Can you sell me this pen?” Three easy steps to develop an initial regulatory, quality, and clinical strategy and then sell it to senior management.
• Establishing and initiating the project
  • Mapping out timelines, responsibilities, and management
  • Records management and documentation
  • Identify ways to incorporate quality planning into a project
• Common myths, best practices, and tools – Lessons learned

The session will cover specific strategies for managing regulatory submissions and leveraging technology to enhance regulatory intelligence and manage complex devices, including AI and software-based medical devices. They will share how MedTech leaders can adapt to digital changes at regulatory bodies and explore how to use AI and digital tools to streamline regulatory processes, expedite market access, and ensure compliance throughout the entire product lifecycle.

Learning Outcomes:

• Learn about the risks of not adapting to the digital advancements in the MedTech landscape.
• Understand how embracing digital transformation in regulatory and compliance activities can offer MedTech companies a strategic advantage by increasing efficiency, reducing costs, and improving patient access to medical devices.
• Explore real-world applications of AI and cloud solutions in regulatory affairs, covering the entire product lifecycle from submissions to post-market changes, along with a framework for achieving regulatory excellence.

• Which regulations and standards are applicable to AI-specific QMS?
  • Do they differ largely from static devices or diagnostics?
• Working examples: end-to-end process of developing QMS with AI/ML
  • Opportunities and challenges in development and application

• Which regulations and standards apply to your device?
• Establishing and monitoring standards in medical device project management
• Incorporating regulatory requirements and considerations throughout the medical device lifecycle (clinical evaluation, safety, and risk management)
• Additional regulatory considerations for project management (quality system requirements, design control, vendor control)
• Common myths, best practices, and tools – Lessons learned

• Key QMS processes to consider for the development of AIMD products
• Challenges and tips to obtaining QMS certification for a small organisation
• Q&A

• Preparing a NHS Wales digital organisation for MDR readiness
  • How do we map out MDR requirements?
  • What obstacles need solutions?
• On-going work
  • What does the future hold?
  • How do we maintain compliance and increase development within a publicly funded resource envelope?

• Risk based approach in classification of Medical Device Software and application of GDPR to databases created thanks to pilot clinical trials, promoted by startup companies and spinoff companies
• Interpreting the AI Regulation for startup and spinoff companies in their development from research project to commercial product
• Impact of the regulatory burden in the relationship with investors

• Practicalities of risk management
• Benefit-risk analysis – the unsolvable equation
• Common myths, best practices, and tools – Lessons learned

• Interpreting PMS requirements for AI/ML
  • Do these differ to traditional medical devices and diagnostics?
• Considerations for static and non-static algorithmic devices and diagnostics
  • On-going data collection
  • Utilising data effectively
• How could cloud based systems enhance PMS plans?

Take the opportunity to have your remaining unanswered questions discussed with our array of experts before the conference ends!

• Metrics used for project management
• Metrics used for notified body performance
• Adapting to change – significant changes will occur
• Understanding full lifecycle expectations
• Discuss how to optimize team performance, communication, and interaction
• Common myths, best practices, and tools – Lessons learned