Watch our webinar to get a glimpse into the future of healthcare in the UK with AI, thanks to the NHS £21 million AI Diagnostic Fund. Explore best-in-breed AI solutions for medical imaging that will improve patient care, as well as gain essential insights on how to strategically utilize spend- from AI tool selection to deployment. Listen to:
Patient wait times in the UK's healthcare system have been a source of concern, reflecting the impact of various challenges on the NHS's ability to provide timely care. Radiology backlogs, in particular, have been exacerbated by shortages in the diagnostic workforce, which has hampered the NHS's recovery from the aftermath of COVID-19. The statistics underscore this issue, revealing that to clear the backlog of patients awaiting CT and MRI scans within a month, the NHS would need to employ an additional 390 radiology consultants, amounting to a 10% increase in the current workforce. This translates to data from NHS England, which indicates that 7.6 million people in England are waiting for hospital treatment, including 1.6 million waiting for diagnostic tests or scans, the highest figure since modern records began in 2007. By automating and expediting image analysis, helping to efficiently process routine scans and more quickly drive toward treatment decisions, AI is well positioned to play a major role in reducing patient backlogs.
NHS Trusts will be able to apply to the AI Diagnostic Fund to accelerate the deployment of the most promising AI imaging and decision support tools to help diagnose patients more quickly for conditions such as cancers, strokes and heart conditions. Know what factors to consider when defining the scope of your AI needs.
The AI Diagnostic Fund will include (but is not limited to) the use of AI tools to analyse Chest X-Rays - the most common tool used to diagnose lung cancer - which is the leading cause of cancer death in the UK. With over 600,000 chest X-rays performed each month in England, the deployment of diagnostic AI tools to more NHS Trusts will support clinicians to diagnose cancer patients earlier, improving patient outcomes.