By Mark Hitchman, Managing Director, Canon Medical Systems UK
Artificial intelligence is frequently presented as the key to transforming healthcare from reactive treatment to proactive prevention. Across the NHS, that vision appears in policy documents, funding programmes, and national strategies.
Yet the reality on the ground tells a different story.
Despite a growing number of AI tools entering clinical use, the long-promised shift towards prevention has not yet materialised at scale. The technology exists. The evidence is building. But in many parts of the health system, adoption remains limited to pilots and small-scale deployments.
The question facing the NHS is no longer whether AI can support earlier diagnosis and more proactive care. Increasingly, that capability is clear. The real challenge is understanding why progress remains slower than expected, and what needs to change for AI to move from promising innovation to everyday clinical practice.
Why AI is critical to prevention
Radiology plays a central role in modern healthcare, where many conditions are first detected, and treatment decisions begin. As the focus on prevention accelerates, demand for diagnostic imaging will only intensify. Screening programmes and earlier diagnosis are fundamental to this shift, but they require scanning large populations, many without any symptoms. While only a small proportion of scans may reveal disease, every image still requires clinical review.
Without new ways of managing this workload, expanding screening programmes risks placing additional pressure on an already stretched radiology workforce. According to the latest Radiology Workforce Census from the Royal College of Radiologists, the NHS could face a consultant radiologist shortfall approaching 39 percent by 2029, while demand for CT and MRI scans continues to rise[1].
The consequences are already clear. Hundreds of thousands of patients wait longer than six weeks for key diagnostic tests each month, while large volumes of imaging studies fall outside NHS reporting targets[2].
This is where AI becomes essential.
AI systems can support imaging teams by triaging scans, prioritising urgent cases, and helping clinicians manage increasingly large volumes of imaging data. Used effectively, these tools could help make large-scale screening programmes far more practical to deliver.
National ambition is clear
This growing reliance on diagnostic imaging, and the need to manage it effectively, is increasingly recognised at a policy level, with AI playing a central role in UK plans to modernise healthcare and support earlier diagnosis.
Through the NHS Long Term Plan and the developing 10 Year Health Plan, policymakers have positioned AI as an important tool for improving productivity and enabling earlier intervention[3]. Significant funding has been committed to support this direction, including more than £100 million through the AI in Health and Care Award[4].
Alongside investment, new initiatives are also beginning to address governance and safety concerns. The NHS AI Knowledge Repository, for example, aims to support evaluation and transparency around AI technologies, while proposals for a dedicated regulatory framework seek to provide greater clarity for developers and healthcare organisations[5].
These steps represent meaningful progress. However, translating national ambition into everyday clinical practice remains a challenge.
Why adoption still stalls
Despite the momentum behind AI innovation, many technologies remain confined to pilot programmes rather than becoming part of routine care.
Workforce capacity is one reason. Implementing new technology requires time, training, and clinical engagement, resources that many departments currently lack.
Funding structures also play a role. Many AI projects begin with short-term innovation funding, which supports experimentation but does not always provide a clear path to long-term deployment. Procurement and governance processes can also slow adoption, particularly when technologies need to integrate with existing hospital systems and meet strict safety and data requirements.
As AI becomes more involved in clinical workflows, governance will also become increasingly important. Health systems will need clear frameworks for monitoring accuracy, auditing performance, and ensuring appropriate clinical oversight.
These challenges explain why many promising technologies struggle to move beyond the pilot phase. However, they also highlight where change is needed to unlock AI’s wider impact.
Moving from pilots to system deployment
If AI is to deliver meaningful impact in NHS imaging, the conversation now needs to move beyond pilots.
Across the UK, many trusts have already tested AI tools in controlled projects. These initiatives have demonstrated both clinical safety and operational value. The next step is turning those isolated successes into scalable systems that support radiology teams across the NHS.
Achieving that shift will require changes in how AI is funded and implemented. Rather than being treated as short-term innovation, AI needs to become part of the digital infrastructure that underpins modern diagnostic services.
Funding models will need to support sustained deployment rather than one-off experimentation. Integration must also be a priority, ensuring AI tools work seamlessly with existing imaging platforms and reporting systems to support clinicians without adding complexity. Crucially, these tools must deliver immediate operational value, helping to reduce workload from the outset rather than adding to the burden on already stretched teams. This will depend not only on technology design, but on creating intuitive, easy-to-use systems that fit naturally into clinical workflows, supported by the right training and guidance for staff.
There is also an opportunity for closer collaboration between the NHS, academia, and industry. Shared evaluation frameworks and clearer governance standards could give trusts greater confidence when selecting and deploying new technologies.
Taken together, these steps would help move AI from promising pilots to practical tools that support everyday clinical care.
Capacity depends on infrastructure
Alongside AI, the condition of existing imaging infrastructure will also shape how far and how fast progress can be made. That’s because a significant proportion of imaging systems across the NHS are now more than a decade old.
Older machines often require longer scan times and may lack the automation and image optimisation tools that newer systems can support. This limits how many patients can be scanned each day and reduces the potential impact of newer technologies.
Modern imaging platforms can significantly improve efficiency through faster scans, better image quality, and integrated AI-enabled tools that support clinicians during the imaging process. In practice, this means more patients can be scanned in less time, helping to increase overall diagnostic capacity.
This matters because capacity will be critical to delivering prevention at scale. AI can help manage imaging demand, but its impact will depend on the infrastructure it sits within. Without investment in modern systems, it will be difficult for AI to support the level of diagnostic capacity that prevention requires.
Building the foundation for prevention
This is where AI’s longer-term potential becomes clear.
If the NHS wants to move from treating illness to preventing it, diagnostic services will need to expand significantly. Screening programmes, earlier testing, and faster diagnosis will all increase the volume of imaging.
AI can help make that shift possible. By supporting clinicians and improving workflow across imaging departments, it can help radiology teams manage demand that would otherwise be impossible to absorb.
The technology is already here. The priority now is creating the conditions that allow it to be deployed at scale.
[1] Royal College of Radiologists, Radiology Workforce Census, 2024: https://www.rcr.ac.uk/news-policy/policy-reports-initiatives/clinical-radiology-census-reports
[2] Royal College of Radiologists, Radiology Delays Worst on Record, 2024: https://www.rcr.ac.uk/news-policy/latest-updates/radiology-delays-worst-on-record-despite-spend-on-private-providers-soaring
[3] Gov.uk, Fit for the Future: 10-Year Health Plan for England, 2023: https://www.gov.uk/government/publications/10-year-health-plan-for-england-fit-for-the-future/fit-for-the-future-10-year-health-plan-for-england-executive-summary
[4] Gov.uk, New Commission to Accelerate NHS AI Use, 2024: https://www.gov.uk/government/news/new-commission-to-help-accelerate-nhs-use-of-ai
[5] Gov.uk, New Commission to Accelerate NHS AI Use, 2024: https://www.gov.uk/government/news/new-commission-to-help-accelerate-nhs-use-of-ai