More than 80% of hospital groups in the U.K. National Health Service (NHS) are now employing radiographers in reporting roles, according to a statement issued on 28 April by the Society of Radiographers (SoR).
Of the NHS trusts and boards that responded to the Clinical Radiology UK Workforce Census 2020 published by the Royal College of Radiologists, 81% said they were employing radiographers in reporting roles -- an increase from the 72% that said the same five years ago. In addition, while training of reporting radiographers is ongoing, there is limited support to backfill their roles to enable increased reporting time, the SoR stated.
Approximately 45% of trusts and health boards polled said they left some categories of images either autoreported or unreported.
Charlotte Beardmore, SoR's director of professional policy, said there was an essential need to expand the imaging workforce to respond to the growing service demand.
"The training pipeline for radiographers needs to increase year on year, both through the traditional and apprenticeship routes, and there must be investment by the NHS in both the development of support roles including those of Assistant Practitioners, and new practitioner roles within service. Investment in the NHS will be essential to support those completing training having roles to go into, within service."
![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)
