The U.K. government has announced on 8 November an investment of 248 million pounds (290 million euros) for National Health Service (NHS) to digitize diagnostic imaging services in an effort to minimize delays and clear the backlog of scans.
The funding will help hospitals share patient results easily and quickly, thus decreasing administrative burden on NHS staff and reducing the time from patients undergoing a diagnostic test and beginning treatment, the government said in a statement.
"Today's multimillion-pound investment will play a big role in leveling up diagnostics services across the country so patients can get faster results and healthcare professionals can get their job done more easily, reducing unnecessary administrative burden and making every taxpayer's pound count," said U.K. Secretary of Health and Social Care Sajid Javid.
The Royal College of Radiologists has welcomed the news and issued a press release.
The cash infusion follows on a 2.3 billion pound investment (2.7 billion euros) the U.K. announced in October that it will make over the course of three years to also improve patient access to diagnostic testing.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
