
The average noncommercial profit for private radiologists in France was calculated at 120,676 euros in 2019, down 2.79% compared with 2018, according to the annual report issued by the autonomous retirement fund for French doctors (Caisse Autonome de Retraite des Médecins de France, CARMF).
This has prompted the union of private radiologists (FNMR) to call for a fairer valuation of radiological acts, allowing more investment in equipment for patients. The union pointed to how the latest figures from the CARMF showed that the income of private radiologists had dropped one place to 11th position on the income scale of private doctors in France, and it thinks the 2020 figures are likely to be worse than those for 2019.
In a statement released on 1 April, the FNMR claims that this statistic counters the government's view that radiologists' incomes are too high. Furthermore, the union states that longstanding government policy based on this perception means that independent radiologists who must invest in their own equipment are finding it increasingly difficult to acquire high-end machines.
These investment restrictions, combined with an insufficient number of authorizations for the installation of MRI machines and CT scanners, have resulted in an increase in appointment times not just for MRI but also for other imaging techniques, the union stated. It further believes that France no longer has the best radiological equipment, impacting patient diagnosis and therapy.
The COVID-19 crisis has illustrated the central role of imaging and shows that the specialty must not be sacrificed in the name of false economies, the union stated. It stressed that in view of these income figures, the FNMR would not accept further devaluation of acts that could accentuate the decline in investments.


![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&fit=crop&h=100&q=70&w=100)







![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&fit=crop&h=112&q=70&w=112)









