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Implementation, Experiences, Impact, and Costs of Artificial Intelligence in Chest Diagnostics: Protocol for a Mixed
Angus I G Ramsay1, Chris Sherlaw-Johnson2, Kevin Herbert3
1Department of Behavioural Science and Health, Institute of Epidemiology and Healthcare, University College London, London, United Kingdom.
JMIR Research Protocols
|October 31, 2025
Summary
This study evaluates artificial intelligence (AI) implementation in NHS chest imaging, examining its impact, costs, and user experiences. Findings will guide best practices for AI adoption in healthcare.
Area of Science:
- Medical Imaging
- Health Informatics
- Artificial Intelligence in Healthcare
Background:
- Artificial intelligence (AI) supports radiology, enhancing lung cancer detection and diagnosis.
- National Health Service England (NHSE) funds AI implementation for chest diagnostic imaging across 12 networks.
- Limited real-world data exists on AI's impact, costs, and user experiences in clinical settings.
Purpose of the Study:
- Evaluate AI tools for chest diagnostic imaging in NHS services.
- Explore AI implementation, service model impacts, and costs.
- Assess experiences of staff, patients, and caregivers with AI in diagnostic imaging.
Main Methods:
- Mixed-methods evaluation using trust-level case studies (in-depth and light-touch).
- Data collection via interviews, observations, documentation analysis, and economic modeling.
- Qualitative data analyzed using Rapid Assessment Procedures and thematic analysis.
Main Results:
- Research and development approvals are complete as of September 2025.
- Data collection has commenced across participating NHS sites.
- Full results are anticipated by the end of February 2026.
Conclusions:
- Identify facilitators and barriers to AI adoption in healthcare.
- Inform best practices for AI implementation and service evaluation.
- Develop frameworks to maximize AI benefits in healthcare settings.
