With a wide variety of commercially available AI products in radiology, it is challenging to effectively implement AI in clinical practice. How to choose? Where to start? A recent NHS publication sheds light on this issue, offering practical guidance for integrating AI into clinical practice.
The research aimed to develop a structured approach for AI deployment in radiology and provide actionable guidance for other radiology departments. It starts with developing a vision for scalable AI adoption. The comprehensive framework that follows includes:
- Assembling a core team with diverse skill sets
- Communicating the vision
- Crafting an AI implementation plan from a clinical need
- Careful vendor selection - a checklist is provided
- Deployment while engaging and educating staff
- Evaluation of outcomes and celebrate success.
- Scaling up and monitoring of the solutions
If you are in the process of implementing commercial AI algorithms, this is one to add to your reading list!
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Artificial intelligence (AI) implementation within the National Health Service (NHS): the South West London AI Working Group experience
Clinical Radiology, 2024
Abstract
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