Two Swiss doctors are criticizing a report released in February and published in JAMA Internal Medicine that suggested Switzerland should abolish its organized mammography screening programs.
The report by the Swiss Medical Board was one-sided and its conclusions should not be implemented, according to an opinion published online on August 25 in JAMA Internal Medicine by epidemiologist Dr. Arnaud Chiolero, PhD, of Lausanne University Hospital and internal medicine physician Dr. Nicolas Rodondi of Bern University Hospital.
"The Swiss Medical Board report, based on the results of previous reviews, emphasized that the evidence in favor of screening mammography is not so strong as commonly thought but included no new evidence in favor or against screening," they wrote. "In our view, current evidence is insufficient to 'abolish' mammography screening programs."
Instead, the medical community should improve the information given to women about the benefits, harms, and uncertainties of screening; conduct new studies on the current impact of mammography screening; and, if women are to be screened, emphasize organized over opportunistic screening (that is, screening recommended to women directly by their doctors), according to Chiolero and Rodondi.
"The report insufficiently emphasizes that assessing the balance between benefits and harms involves a value judgment that each woman should make after she is fully informed about the strengths and weaknesses of screening mammography," they wrote. "On the basis of the same information, some women will choose screening, and others will not."











![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)




