
A highly critical report released on 8 July by the U.K. Parliamentary and Health Service Ombudsman (PHSO) has expressed deep concerns about the state of medical imaging services and called on the government and hospital trusts to prioritize National Health Service (NHS) radiology IT systems and alerts.
The document outlines cases of patients whose diagnoses were missed or delayed following x-rays or scans. Recurrent themes of varied or poor escalation procedures and communication between hospital teams were discovered, according to the "Unlocking Solutions in Imaging" report.
Rob Behrens, the ombudsman, calls for prioritizing digital imaging system improvements as a key patient safety concern and ensuring there is national guidance on radiology reporting roles and procedures. Additionally, he calls on NHS providers to ensure senior staff have allocated time for reflective learning so that hospital radiology leads can regularly collate and share education related to imaging issues.
The report examined 25 complaints relating to failings in NHS imaging services dating back to 2013, including a patient who died after two x-rays were taken one year apart as part of an investigation of existing lung problems. The scans showed abnormal shadowing on the individual's lung, but no further action was taken, delaying a diagnosis of terminal lung cancer.
In another case, a patient was being treated for breast cancer in August 2017 when a scan showed signs of pancreatic cancer. The radiologist did not raise concerns and the patient was not diagnosed with pancreatic cancer until February 2019.
The ombudsman noted that the failures were “not limited to one service, one organisation, or one part of the NHS, but are symptomatic of wider issues.” His recommendations include that digital infrastructure should be treated as a patient safety issue and that the Department of Health and NHS bring in national guidance on who is responsible at every stage of a scan being requested, carried out and reported, as well as time frames. Also, he requests that the Royal College of Radiologists (RCR) ensure its guidance on the reporting of unexpected significant findings is clear and continually updated.
Overall, Behrens said that “the failings outlined in this report show that without a concerted effort to improve imaging, patient safety continues to be at risk." He added that "we have a vital opportunity to learn from the failings and embed system-wide changes to improve imaging in the health service.”
In a press release issued in advance of the report on 7 July, the RCR said it supports the call from the PHSO for the government and hospital trusts to prioritize NHS radiology IT systems and alerts.












![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)





