Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation (10 September 2025)

Summary

Evidence suggests AI might benefit diagnostic services by supporting decision-making, improving detection accuracy, reducing errors, increasing efficiency, and easing workforce burdens. However, little is known about real-world implementation, including procurement, preparation for deployment, experiences of staff, patients, and carers, and impact on effectiveness and costs – due in part to very few studies having been conducted on real-world implementation of AI tools for radiology diagnostics.

Content

In July 2023, the Artificial Intelligence Diagnostic Fund (AIDF) was launched to support AI deployment for chest diagnostics, including lung cancer, across 12 imaging networks (bodies created to support innovation in and standardised use of imaging diagnostics across NHS services across their local regions) and 66 of the 124 acute NHS Trusts (NHS organisations that provide a range of healthcare, e.g. acute hospital care or mental health care, to their local communities) in England.

Improving chest diagnostics and lung cancer detection are national priorities due to high rates of late-stage lung cancer diagnosis, and efficient chest diagnostics may be central to managing any future respiratory disease pandemic.

To help increase understanding of real-world implementation of AI tools in a high priority healthcare setting, the authors analysed procurement and preparation for deployment of AI tools as part of the AIDF programme. The analysis was guided by the Non-adoption, abandonment, scale-up, spread, sustainability (NASSS) framework, which considers the social and technical factors that interact to shape planning, implementation, and uptake of technological innovations.

The analysis addressed the following questions:

• How were AI tools for chest diagnostics procured and deployed as part of AIDF
• Which factors (e.g. context, technology, implementation processes, capacity to implement, and stakeholder characteristics) influenced procurement and early deployment of AI?
• What are the lessons for future real-world implementation and evaluation of AI in
• diagnostics?