Authors' objectives: Artificial intelligence tools simulate aspects of human intelligence. Policy and research highlight artificial intelligence’s potential to support delivery of radiology pathways. In 2023, National Health Service (NHS) England invested £21M to deploy artificial intelligence diagnostic tools for chest X-ray and chest computed tomography in 66 NHS Trusts. Little is known about how artificial intelligence tools are implemented in practice, staff experience of these tools, and their effectiveness and cost. Evaluate evidence on artificial intelligence tools within radiology internationally. Evaluate implementation of artificial intelligence for chest diagnostics in England. Investigate how effectiveness and cost-effectiveness of artificial intelligence for chest diagnostics can be measured. Artificial Intelligence describes computer systems that are trained to help solve problems. People think artificial intelligence may help the National Health Service (NHS) by helping to get the right diagnosis and reducing workload and costs. NHS England funded the use of artificial intelligence for chest scans (X-rays and computed tomography scans) in 66 NHS trusts. Little is known about how artificial intelligence is used; how it affects patients, staff and costs; what healthcare teams, patients, carers and the public think of artificial intelligence being used. We aimed to evaluate the use of artificial intelligence for chest scans in practice.

Authors' results and conclusions: Artificial intelligence tools may support effective, efficient chest diagnostics services. However, several factors should be considered when implementing and monitoring artificial intelligence tools. Implementation and monitoring may be improved through allowing sufficient time for procurement and preparation for deployment or extending capacity to speed completion of these tasks, early and ongoing stakeholder engagement, sufficient resourcing, dedicated expertise and clinical champions, simplifying governance processes, and improving data capacity. Parallels with learning from implementing other innovations suggests that artificial intelligence tools may not offer straightforward solutions anticipated by services and policy-makers.

Authors' methods: Ten-month mixed-methods study (rapid scoping review and empirical study comprising staff interviews, observations and documentary analysis). Findings were analysed using rapid assessment procedures, drawing on qualitative, quantitative and health economic approaches. Our evaluation was also designed to inform phase 2 of our study. Our rapid timeline meant we could not interview patients, carers, and several staff groups at trust and national levels. Delayed deployment meant we could not study implementation in practice or staff experiences.

Project Status: Completed

URL for project: https://www.journalslibrary.nihr.ac.uk/programmes/hsdr/NIHR167339

Year Published: 2026

URL for published report: https://www.journalslibrary.nihr.ac.uk/hsdr/published-articles/GJAR2722

URL for additional information: English

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: England, United Kingdom

DOI: 10.3310/GJAR2722

Organisation Name: NIHR Health Services and Delivery Research programme

Contact Address: NIHR Journals Library, National Institute for Health and Care Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK

Contact Name: journals.library@nihr.ac.uk

Contact Email: journals.library@nihr.ac.uk