In a new report, Israel State Comptroller Matanyahu Englman warns that Health Ministry would not be able to protect the country's medical institutions against cyberattacks, according to an article published May 10 in the Jerusalem Post.
An audit was performed from January to November 2021 that found 13 of 17 medical institutions did not conduct risk assessments on medical equipment and did not have a plan in place for system recovery in the event of hacking.
The Post's report said that in October 2021, during the audit, hackers broke into the servers of Hillel Yaffe Medical Center in Hadera, leading to a large-scale disruption.
However, the audit also found that 13 of the 17 institutions did not perform necessary data security risk assessments on medical equipment and did not have plans in place for a hacking event. Also, 14 institutions allow device manufacturers to connect to MRI and CT devices remotely, one did not regulate how remote connections took place, and two didn't monitor remote connections at all.
On the other hand, the institutions surveyed did a better job of assessing security when purchasing new equipment. In all, 12 of the 17 institutions took data security into account when assessing the purchase of medical devices.
The article goes on to say that Englman recommended that external technicians performing maintenance work at institutions arrive only after coordinating their visit with relevant officials and that an institution employee should accompany maintenance workers at all times.
![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)
