Literature DB >> 31918875

An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging.

Alan Alexander1, Adam Jiang2, Cara Ferreira2, Delphine Zurkiya2.   

Abstract

Radiologists today are under increasing work pressure. We surveyed radiologists in the United States across practice settings, and the overwhelming majority reported an increased workload. Artificial intelligence (AI), which includes machine learning, can help address these issues. It also has the potential to improve clinical outcomes and raise further the value of medical imaging in ways yet to be defined. In this article, we report on recent McKinsey & Company work to understand the growth of AI in medical imaging. We highlight progress in its clinical application, the investments that are backing it, and the barriers to broader adoption. We also offer a view on how the market will develop. AI is set to have a big impact on the medical imaging market and hence on how radiologists work, helping them to speed up scan time, make more accurate diagnoses, and ease their workload. As AI in medical imaging increasingly proves its worth, it is hard to imagine that AI will not ultimately transform radiology.
Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Keywords:  Artificial intelligence; cloud; investments; machine learning; solutions

Mesh:

Year:  2020        PMID: 31918875     DOI: 10.1016/j.jacr.2019.07.019

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  7 in total

Review 1.  Machine learning for sperm selection.

Authors:  Jae Bem You; Christopher McCallum; Yihe Wang; Jason Riordon; Reza Nosrati; David Sinton
Journal:  Nat Rev Urol       Date:  2021-05-17       Impact factor: 14.432

2.  Rehabilitation Treatment of Muscle Strain in Athlete Training under Intelligent Intervention.

Authors:  Yu Qiao; Lei Zhang; Bin Zhang
Journal:  Comput Math Methods Med       Date:  2022-03-29       Impact factor: 2.238

Review 3.  Applications and challenges of artificial intelligence in diagnostic and interventional radiology.

Authors:  Joseph Waller; Aisling O'Connor; Eleeza Rafaat; Ahmad Amireh; John Dempsey; Clarissa Martin; Muhammad Umair
Journal:  Pol J Radiol       Date:  2022-02-25

Review 4.  Challenges and optimization strategies in medical imaging service delivery during COVID-19.

Authors:  Yi Xiang Tay; Suchart Kothan; Sundaran Kada; Sihui Cai; Christopher Wai Keung Lai
Journal:  World J Radiol       Date:  2021-05-28

5.  Ethical Implications of Alzheimer's Disease Prediction in Asymptomatic Individuals through Artificial Intelligence.

Authors:  Frank Ursin; Cristian Timmermann; Florian Steger
Journal:  Diagnostics (Basel)       Date:  2021-03-04

6.  Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence.

Authors:  Thomas C Kwee; Robert M Kwee
Journal:  Insights Imaging       Date:  2021-06-29

Review 7.  Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19.

Authors:  Danai Khemasuwan; Jeffrey S Sorensen; Henri G Colt
Journal:  Eur Respir Rev       Date:  2020-10-01
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.