Literature DB >> 32211946

Implementation and design of artificial intelligence in abdominal imaging.

Hailey H Choi1, Silvia D Chang2, Marc D Kohli3.   

Abstract

Artificial intelligence is a technique that holds promise for helping radiologists improve the care of our patients. At the same time, implementation decisions we make now can have a long-lasting effect on patient outcomes. In the following article, we discuss four areas with unique considerations for implementation of AI: bias, trust, risk, and design. In each section, we highlight applications of AI to abdominal imaging and prostate cancer specifically.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Implementation; Machine learning; Prostate; Safety

Mesh:

Year:  2020        PMID: 32211946     DOI: 10.1007/s00261-020-02471-0

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  21 in total

1.  Is It Time to Stop Paying for Computer-Aided Mammography?

Authors:  Joshua J Fenton
Journal:  JAMA Intern Med       Date:  2015-11       Impact factor: 21.873

Review 2.  Machine Learning for Medical Imaging.

Authors:  Bradley J Erickson; Panagiotis Korfiatis; Zeynettin Akkus; Timothy L Kline
Journal:  Radiographics       Date:  2017-02-17       Impact factor: 5.333

3.  The levels of evidence and their role in evidence-based medicine.

Authors:  Patricia B Burns; Rod J Rohrich; Kevin C Chung
Journal:  Plast Reconstr Surg       Date:  2011-07       Impact factor: 4.730

Review 4.  Deep Learning: A Primer for Radiologists.

Authors:  Gabriel Chartrand; Phillip M Cheng; Eugene Vorontsov; Michal Drozdzal; Simon Turcotte; Christopher J Pal; Samuel Kadoury; An Tang
Journal:  Radiographics       Date:  2017 Nov-Dec       Impact factor: 5.333

5.  From Images to Actions: Opportunities for Artificial Intelligence in Radiology.

Authors:  Charles E Kahn
Journal:  Radiology       Date:  2017-12       Impact factor: 11.105

6.  Why deep-learning AIs are so easy to fool.

Authors:  Douglas Heaven
Journal:  Nature       Date:  2019-10       Impact factor: 49.962

7.  Short-term outcomes of screening mammography using computer-aided detection: a population-based study of medicare enrollees.

Authors:  Joshua J Fenton; Guibo Xing; Joann G Elmore; Heejung Bang; Steven L Chen; Karen K Lindfors; Laura-Mae Baldwin
Journal:  Ann Intern Med       Date:  2013-04-16       Impact factor: 25.391

8.  Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection.

Authors:  Constance D Lehman; Robert D Wellman; Diana S M Buist; Karla Kerlikowske; Anna N A Tosteson; Diana L Miglioretti
Journal:  JAMA Intern Med       Date:  2015-11       Impact factor: 21.873

9.  Implicit Gender Bias and the Use of Cardiovascular Tests Among Cardiologists.

Authors:  Stacie L Daugherty; Irene V Blair; Edward P Havranek; Anna Furniss; L Miriam Dickinson; Elhum Karimkhani; Deborah S Main; Frederick A Masoudi
Journal:  J Am Heart Assoc       Date:  2017-11-29       Impact factor: 5.501

Review 10.  Automation bias and verification complexity: a systematic review.

Authors:  David Lyell; Enrico Coiera
Journal:  J Am Med Inform Assoc       Date:  2017-03-01       Impact factor: 4.497

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.