A Blueprint to Clinical AI Adoption: How NHS Teams Are Operationalising AI in Clinical Practice

There has never been more choice in radiology AI. That is precisely the problem.

With a rapidly growing number of certified radiology AI solutions on the market, and performance that varies depending on scanner type, patient population, and imaging protocol, selecting the right solution has become increasingly complex. Without a neutral way to compare tools on local data, many organisations can end up spending more time navigating the selection process than actually delivering value from AI in practice.

And strong evaluation results do not guarantee clinical impact. The real challenge begins after selection: integrating AI into clinical workflows, aligning teams around its use, and tracking performance in a live environment. This is where many adoption efforts begin to break down, often without clear visibility into what is actually happening in practice.

Lewisham and Greenwich NHS Trust approached this as an end-to-end problem. Facing reporting delays and pressure on emergency care, they implemented a rigorous, BS 30440-aligned framework connecting evaluation, deployment and ongoing monitoring, underpinned by the infrastructure required to run this process reliably in a clinical setting.

The result was not just a successful deployment. It was a measurable shift in patient care and a repeatable model that gives clinical teams the confidence to act and organisations the foundation to scale AI responsibly.

Join us to hear how this was implemented in practice, directly from the team involved.

Why evaluation alone doesn’t lead to clinical impact

Published benchmarks and vendor evidence are a starting point, not a decision. Learn what a rigorous, fair comparison actually requires and what it costs to skip it.

The gap between evaluation and deployment

See what changes when AI solutions move from testing environments into clinical workflows, and how integration, clinical trust, and operational ownership determine whether adoption succeeds.

What governance looks like when it is actually working

Learn how governance frameworks, SOPs, and multidisciplinary collaboration shift AI from a technical implementation to a clinically accountable system embedded in care pathways.

What continuous monitoring looks like in practice

Explore how ongoing performance tracking, discrepancy review, and governance structures enable organisations to maintain safety, manage risk, and adapt AI solutions over time.

How to build a blueprint you can use again and again

See how connecting evaluation, deployment and monitoring creates a foundation that makes every future AI adoption faster, safer and easier to justify.