The U.K. Royal College of Radiologists (RCR) has responded to a Parliamentary question from Wes Streeting, the Shadow Secretary of State for Health and Social Care, regarding the use of AI with MRI.
Streeting, representing the Labour Party, quoted National Health Service (NHS) figures suggesting that upgrading MRI scanners with AI would lead to an additional 3.71 tests per day, translating to an additional 130,000 scans each year if 100 scanners were upgraded.
But these additional scans would require additional radiologists to read them -- and the shortfall of consultant clinical radiologists who report on MRI scans is estimated to reach 40% by 2027, the RCR noted.
"While we welcome anything to speed up patient diagnoses, it's crucial to acknowledge that the root issue lies not in the availability of scanners but in the significant shortage of clinical radiologists," RCR President Dr. Kath Halliday said in a statement released on 20 May.
"[Our] analysis indicates that reporting these scans would require 32,500 hours each year, necessitating an additional 40.6 clinical radiologist consultants. Given the existing 29% shortfall in consultant clinical radiologists, there is a legitimate concern that the current workforce is insufficient to handle this increased volume … the Government must prioritize the training and retention of doctors to ensure that the benefits of these technological advancements can be fully realised without further straining the healthcare system," Halliday said.
![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)
