JMIR Publications

ABSTRACT

Background:

The ability to perform complex tasks has seen artificial intelligence (AI) used to support radiology in clinical settings, including lung cancer detection and diagnosis. Evidence suggests that AI can contribute to accurate diagnosis, reduce errors, and improve efficiency. The National Health Service England (NHSE)-funded Artificial Intelligence Diagnostic Fund (AIDF), is currently supporting 12 NHS Trust imaging networks to implement AI for chest diagnostic imaging. There is however, limited evidence on real-world AI implementation and use, including staff, patient and carer experience, costs and cost-effectiveness. A National Institute for Health and Care Research Rapid Service Evaluation Team Phase 1 evaluation provided insights into the early implementation of these tools and developed a framework for monitoring and evaluation of AI tools for chest diagnostic imaging in practice.

Objective:

This mixed-methods evaluation of Artificial Intelligence tools for chest diagnostic imaging aims to address previous research gaps by exploring the implementation of AI tools for chest diagnostic imaging, the impact and costs of implementing these service models, and the experiences of patients, carers and staff.

Methods:

This will be a mixed-method evaluation of implementation, experiences, impact, and costs of AI for chest diagnostic imaging in NHS services in England, with the evaluation informed by the Major System Change Framework. Trust-level case studies (3 in-depth and up to 9 light-touch) will be performed, including: staff member, patient, carer NHSE AIDF team interviews; meeting observations; analysis of key relevant documentation. Qualitative data will be analysed using Rapid Assessment Procedures and inductive thematic analysis, supplemented by in-depth deductive thematic analysis. Data from case study sites and other relevant sources will be used to assess outcomes at the other sites and for comparators. A pragmatic economic model of the chest diagnostic imaging pathway will be developed to estimate key costs and resource use associated with AI tool deployment. Together with input from national stakeholder and staff workshops the study findings will then be finalised for reporting.

Results:

As of July 28th 2025, we are currently confirming trust-level research and development approvals with participating sites. Where this has been completed, the data collection process has commenced. Results are expected to be reported by the end of 2025.

Conclusions:

The study will provide new insights into the facilitators and barriers to the adoption of AI technology in healthcare and the perceptions of both the general public and healthcare staff on its use. It will also inform best practice in approaches for service performance evaluation, for the implementation of AI into existing care pathways, and for the development of models to best support evidence-based decision making. It will thus establish a framework upon which the greatest benefits of the use of AI in healthcare can be realised