NHS Trusts can apply for funding for imaging and decision-support AI tools to help diagnose patients more quickly for conditions such as lung cancer and stroke until the 4th September.
Book your free 30 minute consultation with our
UK team to discuss:
- An overview of the AI Diagnostic Fund application process
- How decision-support AI solutions could drive improvements across the clinical workflow
- Whether AI could support NHS long term goals at your Trust
Book a free 30 minute consultation with our UK team
About Annalise Enterprise
Annalise Enterprise is an award-winning decision-support AI solution that uses deep learning to identify the suspected presence of up to 124 findings on chest X-rays and 130 findings on non-contrast head CT images, including a number of time sensitive pathologies.
Global deployments, including across NHS England and NHS Scotland
Findings identified providing value across different care pathways including lung cancer, stroke, chronic diseases and never events.
Million patient cases processed and counting
How could the clinical application of Annalise Enterprise support NHS Trusts?
Detect Lung Cancer Earlier
When used as a clinical assistant tool, decision-support AI solutions, such as Annalise Enterprise CXR have been shown to improve the accuracy of lung nodule detection.
Improve Lung Cancer
The National Optimal Lung Cancer Pathway aims to have cancer ruled out or diagnosed within 28 days; however, many trusts have significant delays between the initial CXR and follow-up CT chest examination. Annalise Enterprise is currently being used in the NHS to facilitate access to same day CT by flagging CXRs with suspected lung cancer findings before the patient leaves the department.
Support the Early
Diagnosis of Stroke
Annalise Enterprise CTB improves diagnostic accuracy when used as an assist-tool by 32%, averaged across all 130 findings. Annalise Enterprise CTB identifies 22 findings suggestive of haemorrhagic and ischaemic strokes. It could be used to detect subtle bleeds and early features of ischaemia and drive quality improvement by enabling earlier detection and intervention for our most critically ill patients, via AI-assisted worklist triage.
Address the imaging backlog
98% of clinical directors in the UK are concerned about the impact of shortages on backlogs and delays and 90% are concerned that workforce shortages will affect patient safety (RCR Census, 2023). AI-enhanced worklist triage can analyse a backlog of CXR and non-contrast head CT images (NCCTB) for 124 and 130 findings respectively and ‘flag’ priority patients with findings that may require urgent treatment for priority reporting. Using AI as an assistant tool during image interpretation has also been shown to speed up reporting time by 10% on chest X-rays (Seah et al. Lancet 2021) and 11% of NCCTB images (Buchlak, Q. et al. European Radiology, 2023).
“To clear the backlog of patients waiting for CT and MRI scans within a month, the NHS in England would have to employ 390 radiology consultants, the equivalent of a 10% increase in the current workforce.”
Dr Katharine Halliday, President of The Royal College of Radiologists