Jeffrey SorensonAIUtilization is the new innovation in healthcare enterprise AIEnterprise artificial intelligence (AI) has shown tremendous promise in the field of radiology, specifically around assistive intelligence. But what can be done to improve confidence in its use? Jeff Sorenson explains.February 1, 2021Page 1 of 1Top StoriesUltrasoundSuspended suits offer protection for interventional echocardiographersSuspended lead suits reduce head-level radiation doses to interventional echocardiographers compared with traditional lead aprons.CTGoogle AI Studio shows potential for AI-based lung cancer detectionRisk ManagementPodcast: Whose duty is it to ensure MRI safety?Womens ImagingThe future of BI-RADS includes AISponsor ContentModernizing Radiotherapy Workflows: Lessons in Safety, Consistency, and Adoption at Scale
![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)
