AIMC Topic: Quality Improvement
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Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial.
Scientific reports
Sep 13, 2021
Artificial intelligence technology is becoming more prevalent in health care as a tool to improve practice patterns and patient outcomes. This study assessed ability of a commercialized artificial intelligence (AI) mobile application to identify and ...
Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different?
The Canadian journal of cardiology
Aug 28, 2021
Innovations in health care are growing exponentially, resulting in improved quality of and access to care, as well as rising societal costs of care and variable reimbursement. In recent years, digital health technologies and artificial intelligence h...
Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study.
Journal of applied clinical medical physics
Jun 23, 2021
PURPOSE: In an ultrahigh-resolution CT (U-HRCT), deep learning-based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation...
A Gastrointestinal Endoscopy Quality Control System Incorporated With Deep Learning Improved Endoscopist Performance in a Pretest and Post-Test Trial.
Clinical and translational gastroenterology
Jun 15, 2021
INTRODUCTION: Gastrointestinal endoscopic quality is operator-dependent. To ensure the endoscopy quality, we constructed an endoscopic audit and feedback system named Endo.Adm and evaluated its effect in a form of pretest and posttest trial.
Current status and limitations of artificial intelligence in colonoscopy.
United European gastroenterology journal
Jun 7, 2021
BACKGROUND: Artificial intelligence (AI) using deep learning methods for polyp detection (CADe) and characterization (CADx) is on the verge of clinical application. CADe already implied its potential use in randomized controlled trials. Further effor...
Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.
Chest
Jun 4, 2021
BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules.
Natural language processing for the surveillance of postoperative venous thromboembolism.
Surgery
Jun 3, 2021
BACKGROUND: The objective of this study was to develop a portal natural language processing approach to aid in the identification of postoperative venous thromboembolism events from free-text clinical notes.
Applying Contemporary Machine Learning Approaches to Nutrition Care Real-World Evidence: Findings From the National Quality Improvement Data Set.
Journal of the Academy of Nutrition and Dietetics
Apr 23, 2021
Using real-world data from the Academy of Nutrition and Dietetics Health Informatics Infrastructure, we use state-of-the-art clustering techniques to identify 2 phenotypes characterizing the episodes of nutrition care observed in the National Quality...
Applying Machine Learning Across Sites: External Validation of a Surgical Site Infection Detection Algorithm.
Journal of the American College of Surgeons
Apr 5, 2021
BACKGROUND: Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. To...
Missed Incidental Pulmonary Embolism: Harnessing Artificial Intelligence to Assess Prevalence and Improve Quality Improvement Opportunities.
Journal of the American College of Radiology : JACR
Feb 16, 2021
PURPOSE: Incidental pulmonary embolism (IPE) can be found on body CT. The aim of this study was to evaluate the feasibility of using artificial intelligence to identify missed IPE on a large number of CT examinations.
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