Latest Blog Posts
Navigating MDR: Fresh Insights and Updates

Automating Regulatory Compliance: The Future of Quality and Regulatory?
Summary Artificial intelligence is rapidly becoming part of the quality and regulatory landscape. From drafting procedures and technical documentation to auditing management systems and analysing

prEN 18286, future of the EU’s AI management system standard
Summary The regulatory landscape for AI-enabled medical devices continues to evolve, and one of the latest developments attracting attention is prEN 18286, the proposed European

Meet the Team: Lauren Perez
At MedQAIR, we know that strong regulatory and quality systems are built on both experience and continuous learning. As medical devices, software, and AI-enabled technologies

Cybersecurity Resilience Act (CRA, 2024/2847) and Health Software
Summary Cybersecurity has become a central topic across the European digital regulatory landscape. While medical device manufacturers have already been navigating cybersecurity requirements under the

AI Act Section A to B: Minimal change or far-reaching consequences?
Summary The European Parliament’s proposal to move product-regulated AI systems from Section A to Section B of the AI Act may initially sound like a

Article 5(5) Redesigned: Sharing In-House Medical Software Across Hospitals
Summary When the MDR and IVDR entered into force, Article 5(5) created a specific framework for medical devices developed and used within health institutions. The

AI/ML Systems, preparing an FDA submission Part III: ‘The submission’
Summary By the time teams reach the submission phase, the assumption is often that the hardest decisions are behind them. In Part III, Leon challenges

AI/ML Systems, preparing an FDA submission Part II: ‘Pre-sub strategy’
Summary In Part II of this series, Leon shifts the focus from initial regulatory positioning to one of the most underestimated and often misused steps

AI/ML Systems, preparing an FDA submission – Part I: ‘Initial Strategy’
Summary FDA submissions for AI/ML-enabled medical devices rarely fail because of a single mistake – they unravel due to a series of early assumptions that