Artificial intelligence (AI) implementation within the National Health Service (NHS): The South West London AI Working Group experience
Open accessAbstract
In the rapidly evolving field of artificial intelligence (AI) for radiology, with a plethora of vendor options and use-cases and evidence claims to sift through, the pressing question is how to effectively implement the right tool for enhanced patient care? This article presents a structured approach to AI deployment, drawing from a comprehensive case study in South West London. We underscore the necessity of forming a dedicated AI team with a clear vision and assertive leadership to navigate such complexities. Central to our discussion is the significance of crafting an AI implementation plan, with an overarching aim to augment patient care, promote operational efficiency, and lay down standardized protocols for seamless AI adoption. By presenting a blueprint for AI implementation within the National Health Service (NHS), we intend to demystify the process for radiology departments across the UK, enabling them to make informed decisions and empowering their staff to embrace and leverage AI responsibly ensuring that patient welfare remains at the heart of innovation. Thus, having a framework to follow when implementing an AI solution that addresses a vision for scalable adoption, core team members with diversity of skillset, staff engagement and education, plan for vendor selection, and change management is crucial for success.
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Much of the review literature addressed governance architectures, robustness concepts, implementation barriers, monitoring frameworks, MLOps, or deployment-proximal evaluation, rather than mature evidence generated during sustained routine deployment.
“Much of the review literature instead addressed governance architectures, robustness concepts, implementation barriers, monitoring frameworks, MLOps, or deployment-proximal evaluation, rather than mature evidence generated during sustained routine deployment [18,19,20,29,35,37,47,49].”
From Beyond Model Development in Healthcare AI: Post-Development Robustness, Post-Deployment Monitoring, and Lifecycle Governance—A Scoping Review of Reviews (2026) · cites this paper as [47]
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References 15 · Cited by 17
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- Authors
- Shelmerdine, S. C., Togher, D., Rickaby, S., & Dean, G.
- Affiliations
- Great Ormond Street Hospital (GB)
- St Helier Hospital (GB)
- Corresponding author
- Shelmerdine, S.C.
- Venue
- Clinical Radiology
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 79
- Issue
- 9
- Pages
- 665-672
- ISSN
- 0009-9260 (ISSN-L)
- OpenAlex
- W4399245836
- Cited by
- 17
- References
- 15
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 2
- FWCI
- 1.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Clinical Radiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute for Health and Care Research (NIHR-301322)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- AI implementation, artificial intelligence in radiology, National Health Service, supplier selection, change management, employee engagement, AI adoption framework, patient care, South West London
- MeSH terms
- Artificial Intelligence, Humans, London, Radiology, State Medicine
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