AIMC Topic: Workflow
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Automated sample preparation with SP3 for low-input clinical proteomics.
Molecular systems biology
Jan 1, 2020
High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot ...
Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence.
Academic radiology
Jan 1, 2020
RATIONALE AND OBJECTIVES: Our primary aim was to improve radiology reports by increasing concordance of target lesion measurements with oncology records using radiology preprocessors (RP). Faster notification of incidental actionable findings to refe...
How the FDA Regulates AI.
Academic radiology
Jan 1, 2020
Recent years have seen digital technologies increasingly leveraged to multiply conventional imaging modalities' diagnostic power. Artificial intelligence (AI) is most prominent among these in the radiology space, touted as the "stethoscope of the 21s...
Accelerating the Translation of Artificial Intelligence From Ideas to Routine Clinical Workflow.
Academic radiology
Jan 1, 2020
PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2020
The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location within the tissue. Recently, a number of deep learning diagnostic aids have been developed to class...
Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience.
Journal of the American College of Radiology : JACR
Sep 1, 2019
A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow.
Journal of the American College of Radiology : JACR
Sep 1, 2019
Ultrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)-powered ultrasound is becoming more...
The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network.
Journal of the American College of Radiology : JACR
Sep 1, 2019
Recent advances in machine learning and artificial intelligence offer promising applications to radiology quality improvement initiatives as they relate to the radiology value network. Coordination within the interlocking web of systems, events, and ...
Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology.
Journal of the American College of Radiology : JACR
Sep 1, 2019
Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common i...
Interactive Machine Learning for Laboratory Data Integration.
Studies in health technology and informatics
Aug 21, 2019
Laboratory data collected in the electronic health record as part of routine care can be used in secondary research. For example, the US Department of Veterans Affairs maintains a data warehouse covering over 20 million individuals and 6.6 billion la...
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