Dr. Stephen Herman[email protected]ISHow to evaluate, use imaging clinical decision supportAlthough recent research found that radiology clinical decision-support (CDS) software did not result in imaging orders being sent to other institutions, it's natural for hospitals to still be wary of annoying their referring physicians. Much can be done, though, to win clinician support and succeed with CDS, explains Dr. Stephen Herman from MedCurrent.July 7, 2016Page 1 of 1Top StoriesCTGoogle AI Studio shows potential for AI-based lung cancer detectionThe model identified lung malignancies and produced consistent, structured reports, but needs further refinement.Risk ManagementPodcast: Whose duty is it to ensure MRI safety?Womens ImagingThe future of BI-RADS includes AIMRINew MRI scoring system predicts early liver cancer recurrenceMRIUnstable brain networks may drive multiple sclerosis fatigue
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
