Why we're engaging with the AI Airlock

Gemma Warren, Policy Manager, Data and AI

Artificial intelligence is moving quickly in health and care - and for organisations like the HRA, that raises an important question: how do we make sure innovation is happening safely, ethically and with patients at the centre? That’s one of the reasons we’ve been closely engaging with the MHRA’s AI Airlock over the past two years.

The Airlock is a space where regulators and developers can come together to work through some of the trickiest questions around AI in healthcare. It’s deliberately designed as a kind of ‘safe testing ground’ - somewhere you can explore emerging technologies, challenge assumptions and develop regulatory thinking before things are widely deployed.

For us at the HRA, it’s been a valuable way of understanding what’s happening on the ground.

Seeing AI development up close

One of the biggest benefits of engaging with the Airlock has been the opportunity to see how AI tools are being developed in practice - often at a very early stage. That’s important because many of the developers involved aren’t necessarily thinking about their work as ‘research’. They’re building, testing and refining products, sometimes using patient data or aiming for clinical use, but without always being clear on when they might need to engage with processes like ethical review. From our perspective, that creates a bit of a grey area.

A lot of what’s happening in AI development sits right on the boundary of research and that boundary isn’t always obvious. One of the things we’re working on as an organisation is making that clearer, including through our “Is it research?” work, so people know when to come to us and what support they might need. Conversations through the Airlock, and other key initiatives have helped us understand where those dividing lines really are.

It’s not always clear when the scope changes

Something that has come through strongly - particularly in workshops earlier this year - is how difficult it can be to categorise AI systems in healthcare. We’ve looked at examples like clinical note-taking tools or chatbots supporting weight loss and wellbeing. It is not always clear when a product moves from administrative and wellness support to regulated medical use.

The way a system is intended to provide information, what it includes and emphasises and how it frames a response can change what the output is meant to be used for and how it is understood, and can start to shift something from being a general tool into something that looks much more like a medically intended purpose. That matters because once something is performing a clinical function, the expectations around evidence, safety and oversight change significantly.

What’s become clear is that existing definitions don’t always neatly fit AI, especially when tools evolve over time or take on new functions. That’s exactly the kind of challenge the Airlock is designed to explore, and one that the HRA has also been exploring.