Jeffrey Sorenson

AI
Utilization is the new innovation in healthcare enterprise AI
Enterprise 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, 2021
2019 04 05 22 13 3278 Computer Digital Cockpit 400
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Top Stories
Shoulder Joint 400
Ultrasound
Could AI help standardize MSK ultrasound?
An AI algorithm showed success in providing accurate, real-time guidance for shoulder imaging.
X Ray Radiography Tech Patient
Practice Management
Researchers to develop 'patient-centered' imaging assessment tool
Radiologist Pamela Woodard Academy President Hill Day 2026
Imaging Informatics
Hill Day 2026 highlights weight of imaging data in biomedical research
Berg, Wendie Minnie Cast Thumbnail
Womens Imaging
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Modernizing Radiotherapy Workflows
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(A) Overview of study participants with different clinical phenotypes depicted in distinct colors: Limbic encephalitis (LE)/temporal lobe epilepsy (TLE): Blue; cerebellar ataxia (CA): Green; stiff person syndrome (SPS): Red, including overlapping clinical features with an overview of the analyzed parameter; generated in Biorender. (B) F-18 F-DED uptake, as visualized by SUVr with global mean scaling, is shown for three representative patients with GAD65-AIE: Patient #6 with LE/TLE, showing mesiotemporal F-18 F-DED uptake; patient #16 with overlapping clinical features of CA, SPS, and LE, showing both mesiotemporal and cerebellar F-18 F-DED uptake; and patient #17 with isolated SPS showing no increased F-18 F-DED uptake. Additionally, the average F-18 F-DED uptake of eight controls is shown on the right.
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Non-Hispanic white, Asian, and uninsured women ages 40 to 49 years used mammography less between 2010 and 2022.
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Representative images of participants with difficulty with breath holding and arrhythmia. In the lower right corner of A–L, the position coordinates of the images are marked. Short-axis (SA) view images in the end-systole (A–F) and end-diastole (G–L) phases in participants with atrial fibrillation. (A–C, G–I) Balanced steady-state free precession (bSSFP) cardiac cine. (D–F, J–L) Deep learning–enhanced compressed sensing (AI-CS) single-shot cardiac cine. In contrast to AI-CS single-shot cardiac cine, endocardial contours were challenging to identify on conventional segmented cine in midventricular and apical sections. (M) Line graph of time curve during the whole cardiac cycle obtained from bSSFP cardiac cine. (N) Line graph of time curve during the whole cardiac cycle obtained from AI-CS single-shot cardiac cine. LV = left ventricle; 2D = two-dimensional.
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