Artificial intelligence (AI) implementation within the National Health Service (NHS): the South West London AI Working Group experience.

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.

Reporting radiographer peer review systems: A cross-sectional survey of London NHS Trusts.

INTRODUCTION: Peer review is frequently incorporated within radiographer reporting services. The aim of this study is to establish peer review systems used for radiograph reports provided by reporting radiographers in London. METHODS: An online cross-sectional survey of NHS diagnostic imaging departments was performed. Reporting radiographer demographics (number, frequency of reporting, scope of practice) and systems used to provide peer review of radiograph reports (review frequency, case selection, volume, outcome measure, practitioner performing the review) were collected. RESULTS: Thirteen eligible responses were received (61.9% response rate). Variability was found between Trusts in the number of reporting radiographers, frequency of reporting sessions and scope of practice. Most Trusts (9 of 13, 69.2%) have active peer review systems for radiographer reporting. All peer review systems use random case selection, most often performed on a monthly basis. Both a fixed number or a percentage of cases reported were used, with true positive, true negative, false positive, false negative the most frequent outcome measure. Of the 12 Trusts that have or are planning a peer system, all currently or plan to use reporting radiographers to conduct the review, with five (41.2%) also using consultant radiologists. CONCLUSION: Peer review of radiographer reporting is common in London NHS Trusts although there is variation in the methods used. IMPLICATIONS FOR PRACTICE: Radiographer reports frequently undergo peer review. Standardisation of reporting radiographer peer review systems should be considered, and a standardised systematic peer review system has been proposed.