โก Quick Summary
This study outlines a mixed methods evaluation of Artificial Intelligence (AI) in chest diagnostics, focusing on its implementation, impact, and costs within NHS services in England. The findings aim to bridge existing research gaps and enhance the understanding of AI’s role in improving diagnostic accuracy and efficiency.
๐ Key Details
- ๐ Focus: Implementation of AI tools for chest diagnostic imaging
- ๐ฅ Setting: NHS services in England, supported by the NHSE-funded AIDF
- ๐ Methodology: Mixed methods evaluation including case studies, interviews, and thematic analysis
- ๐ Timeline: Data collection commenced in September 2025, with results expected by February 2026
๐ Key Takeaways
- ๐ค AI technology is being integrated into chest diagnostics to enhance accuracy and reduce errors.
- ๐ก The study aims to evaluate the experiences of patients, caregivers, and staff regarding AI implementation.
- ๐ A pragmatic economic model will estimate costs and resource use associated with AI deployment.
- ๐ Insights will be gathered on facilitators and barriers to AI adoption in healthcare.
- ๐ The findings will inform best practices for integrating AI into existing care pathways.
- ๐ฃ๏ธ Stakeholder engagement will play a crucial role in shaping the evaluation framework.
- ๐ Results are anticipated to be reported by the end of February 2026.
๐ Background
The integration of Artificial Intelligence in healthcare has shown promise, particularly in radiology for tasks such as lung cancer detection. Despite its potential, there remains a lack of comprehensive evidence regarding the real-world implementation of AI technologies, especially concerning the experiences of healthcare staff and patients, as well as the associated costs. This study seeks to fill these gaps by evaluating the deployment of AI in chest diagnostics within the NHS framework.
๐๏ธ Study
This evaluation is designed as a mixed methods study, utilizing both qualitative and quantitative approaches to assess the implementation, experiences, impact, and costs of AI tools in chest diagnostic imaging. The research will include in-depth case studies at three NHS trusts, supplemented by light-touch studies at up to nine additional sites. Data collection will involve interviews with staff, patients, and caregivers, alongside observations and documentation analysis.
๐ Results
As of September 2025, the study has successfully completed trust-level research and development approvals, and data collection is currently underway. The results, expected by February 2026, will provide valuable insights into the effectiveness and efficiency of AI tools in clinical practice, as well as the overall experience of stakeholders involved.
๐ Impact and Implications
The outcomes of this study are poised to significantly influence the adoption of AI technologies in healthcare. By identifying the facilitators and barriers to AI implementation, the research will contribute to the development of best practices for integrating AI into existing care pathways. This could lead to improved diagnostic accuracy, reduced healthcare costs, and enhanced patient outcomes, ultimately transforming the landscape of chest diagnostics.
๐ฎ Conclusion
This mixed methods evaluation represents a crucial step towards understanding the real-world implications of AI in healthcare. By focusing on the experiences of patients, caregivers, and healthcare staff, the study aims to establish a robust framework for the effective integration of AI technologies in chest diagnostics. The future of AI in healthcare looks promising, and continued research in this area is essential for maximizing its benefits.
๐ฌ Your comments
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Implementation, Experiences, Impact, and Costs of Artificial Intelligence in Chest Diagnostics: Protocol for a Mixed Methods Evaluation.
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 National Health Service (NHS) networks to implement AI for chest diagnostic imaging. There is, however, limited evidence on real-world AI implementation and use, including staff, patient, and caregiver experience, and 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 AI 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, caregivers, and staff.
METHODS: This study 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, and caregiver; NHSE AIDF team interviews; meeting observations; and analysis of key relevant documentation. Qualitative data will be analyzed 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 stakeholders and staff workshops, the study findings will then be finalized for reporting.
RESULTS: As of September 2025, trust-level research and development approvals with participating sites are complete, and data collection has commenced. Results are expected to be reported by the end of February 2026.
CONCLUSIONS: The study will provide new insights into the facilitators and barriers to the adoption of AI technology in health care and the perceptions of both the general public and health care staff on its use. It will also inform best practices 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 health care can be realized.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/81421.
Author: [‘Ramsay AIG’, ‘Sherlaw-Johnson C’, ‘Herbert K’, ‘Bagri S’, ‘Bodea M’, ‘Crellin N’, ‘Elphinstone H’, ‘Halliday A’, ‘Hemmings N’, ‘Lawrence R’, ‘Lobont C’, ‘Ng PL’, ‘Lloyd J’, ‘Massou E’, ‘Mehta R’, ‘Morris S’, ‘Shand J’, ‘Walton H’, ‘Fulop NJ’]
Journal: JMIR Res Protoc
Citation: Ramsay AIG, et al. Implementation, Experiences, Impact, and Costs of Artificial Intelligence in Chest Diagnostics: Protocol for a Mixed Methods Evaluation. Implementation, Experiences, Impact, and Costs of Artificial Intelligence in Chest Diagnostics: Protocol for a Mixed Methods Evaluation. 2025; 14:e81421. doi: 10.2196/81421