AI and digital health are transforming diagnostics, promising unprecedented accuracy and efficiency in in vitro diagnostic (IVD) devices. But with innovation comes a labyrinth of regulatory challenges. How do you ensure that your AI-powered IVD not only meets market needs but also clears regulatory hurdles in the EU, US, and beyond?

 In this session, we’ll unravel the complexities of integrating AI into IVDs while maintaining compliance with evolving global frameworks. From algorithm transparency and data integrity to risk management and post-market surveillance, we’ll map out actionable strategies for navigating the regulatory maze. 

Real-world case studies will shed light on common pitfalls and proven approaches, giving you the insights you need to mitigate risks, streamline development, and future-proof your AI-enabled diagnostics.

Join us to discover how to align cutting-edge technology with robust compliance – without getting lost in the regulatory maze.

This session will explore how AI agents can help regulatory and clinical teams reduce the time and effort needed to produce compliant clinical evaluations under MDR. Drawing from real-world examples, I’ll share how automation and intelligent workflows can simplify article screening, data extraction, and evidence appraisal, without compromising quality. Attendees will walk away with practical tools, use cases, and a realistic view of what AI can do today in the context of clinical evaluation.

The integration of artificial intelligence (AI) into healthcare presents unique regulatory challenges, from managing adaptive algorithms to addressing risks like automation bias and model drift. Traditional regulatory frameworks, designed for static technologies, often struggle to accommodate the complexity and dynamism of AI as a medical device (AIaMD). In response, sandboxes offer a novel, collaborative environment for AI developers, regulators, and clinical stakeholders to test and refine regulatory approaches in various environments (theoretical and real world). This panel contribution shares key insights from the AI Airlock pilot, which supports selected AIaMD developers through tailored regulatory experimentation [and explores the EU’s approach – TBC].

Using a regulatory challenge-led framework, the AI Airlock pilot explored issues across the product lifecycle, including AI errors, validation, explainability, performance drift, and human-AI interaction. Now in its second phase, it continues to explore some of the most pressing regulatory challenges with AIaMD today. The session will discuss how sandboxes can support regulatory learning while protecting patient safety, foster evidence generation for novel technologies, and inform future policy. Reflections will also cover practical lessons on setting up a sandbox and cross-sector collaboration.

I will aim to provide the notified body’s perspective on these challenges, and will aim to cover areas such as describing and defining the software and its intended purpose, classification, establishing the software in relation to the State of the Art, Clinical Data, Appraisal and Analysis, and PMCF.

The intended purpose of a device goes beyond regulatory concerns; it also drives health economic considerations and can underlie intellectual property issues, all of which inform market access strategy. Aligning the intended purpose across all domains is crucial for successful market entry.