Here's a question for you: what makes a good radiologist? Patients and members of the public -- if they have any idea at all what radiologists do -- tend to focus on eyesight and observational skills. Doctors in training have a similar view that good radiology is all about spotting everything on an image or set of images.
With a little experience, we come to realize that "spotting" is only part of the process. Yes, the observation is important, but it is the intelligent integration of the visual clues with other available information and the subsequent communication of our synthesized interpretation that results in what I would call "good radiology." We might add a further requirement which is that good radiology is that which leads to the best possible outcome for the patient.
Dr. Giles Maskell enjoys a break at the European Society of Breast Imaging (EUSOBI) annual meeting in Valencia, Spain, in 2023. Photo courtesy of EUSOBI.
The process starts well before the examination is performed -- it's easy to forget that the best possible outcome for the patient might be for the test not to be performed at all. Every medical intervention -- surgical, medical, or diagnostic testing -- carries the potential to cause harm. The good radiologist is able and willing to point out those occasions when the risks outweigh the possible benefits of carrying out the examination and to suggest an alternative strategy.
Nowhere is this clearer than in interventional radiology. In this context, hand/eye coordination might seem to be the most important attribute, but most experienced interventional radiologists cite good decision-making as being even more critical. There is a much-quoted maxim in surgery that good surgeons know how to operate, better surgeons know when to operate and the best know when not to operate.
Our clinical colleagues, when asked, consistently prefer radiologists who are prepared to come off the fence and offer an opinion -- within a suitable and consistent framework of doubt or certainty -- over those who will only provide a long list of differentials. This was drummed into me early in my career by a physician who used to speak scornfully of the differential diagnosis as "a list of conditions that the patient hasn't got." He had a point.
Uncertainty is inherent in an interpretive discipline such as radiology, so 100% accuracy is not an option. With experience, we get to understand our own tolerance of uncertainty and where each of us stands on a line between never under-calling and never over-calling. Importantly, our clinical colleagues learn this too and know which radiologist to go to when they want to be reassured that all is well and which to ask when their instinct is to do further tests.
It is tempting to think that there might be a place on this spectrum that offers the best possible balance between sensitivity and specificity and that we might use that to define what is "good," but I think the truth is that, away from the extremes, "good radiology" can occur at any point on the line.
Alongside the interpretation of the images, the good radiologist needs to understand enough of the clinical background and management options to know which findings -- positive and negative -- are likely to be important in any particular case, which to stress and which to disregard. Then of course all this must be conveyed in terms which make sense to any reader of the report and will make a positive contribution to achieving the best possible outcome for the patient.
Nobody said it was easy! I suppose the message here -- as if you didn't know it already -- is that there is a lot more to good radiology than having a sharp pair of eyes and a long differential.
Dr. Giles Maskell is a consultant radiologist at Royal Cornwall Hospitals National Health Service (NHS) Trust, Truro, U.K. He is a former president of the U.K. Royal College of Radiologists. Competing interests: None declared.
The comments and observations expressed herein do not necessarily reflect the opinions of AuntMinnieEurope.com, nor should they be construed as an endorsement or admonishment of any particular vendor, analyst, industry consultant, or consulting group.



![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)








