Low-level radiation exposure is less harmful to health than other modern lifestyle risks, according to research published on 13 September in the Proceedings of the Royal Society B.
A team of researchers from the University of Oxford gathered evidence on health risks from low-level ionizing radiation and held a one-day workshop to develop a consensus on a series of statements that describe the health effects of exposure to low-level radiation.
The statements cover a series of radiation-related issues, such as environmental exposure to radon, the conclusions of the Japanese study of atomic bomb survivors, and what's currently known about the health effects of exposure to both high and low doses of radiation.
The group found the overall risk to human health from low-level radiation exposure is small, especially when compared with general risks such as obesity, smoking, and air pollution.
For example, if 100 people were each briefly exposed to 100 mSv of radiation, one of them on average would be expected to develop a radiation-induced cancer over a lifetime, while 42 of them would be expected to develop cancer from other causes, according to a team led by Dr. Angela McLean. As context, radiation dose from a spine CT is 10 mSv, and the average dose from natural background radiation in the U.K. is 2.3 mSv each year.
With respect to medical radiation, the group noted that previous studies have found links in risk between leukemia and brain cancer in children who received CT scans, but there have been confounding factors. The group recommended that "the health benefits of radiation use in medicine must outweigh any radiation exposure risks."
McLean and colleagues plan to conduct further research to better understand the genetic healthcare implications of radiation exposure and the biological basis of the damage from radiation to DNA and cells, Oxford said in a statement.










![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)






