Literature DB >> 32059956

Noninterpretive Uses of Artificial Intelligence in Radiology.

Michael L Richardson1, Elisabeth R Garwood2, Yueh Lee3, Matthew D Li4, Hao S Lo5, Arun Nagaraju6, Xuan V Nguyen7, Linda Probyn8, Prabhakar Rajiah9, Jessica Sin10, Ashish P Wasnik11, Kali Xu12.   

Abstract

We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.
Copyright © 2020 The Association of University Radiologists. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Deep learning; Radiology applications; Radiology education

Year:  2020        PMID： 32059956     DOI： 10.1016/j.acra.2020.01.012

Source DB:  PubMed          Journal:  Acad Radiol        ISSN： 1076-6332            Impact factor:   3.173


  8 in total

1.  Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization.

Authors:  Ross W Filice; Anouk Stein; Ian Pan; George Shih
Journal:  J Digit Imaging       Date:  2022-01-11       Impact factor: 4.056

Review 2.  Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.

Authors:  Yasasvi Tadavarthi; Valeria Makeeva; William Wagstaff; Henry Zhan; Anna Podlasek; Neil Bhatia; Marta Heilbrun; Elizabeth Krupinski; Nabile Safdar; Imon Banerjee; Judy Gichoya; Hari Trivedi
Journal:  Radiol Artif Intell       Date:  2022-02-02

Review 3.  Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective.

Authors:  Cheryl Beegle; Navid Hasani; Roberto Maass-Moreno; Babak Saboury; Eliot Siegel
Journal:  PET Clin       Date:  2022-01

4.  Noninvasively predict the micro-vascular invasion and histopathological grade of hepatocellular carcinoma with CT-derived radiomics.

Authors:  Xu Tong; Jing Li
Journal:  Eur J Radiol Open       Date:  2022-05-16

5.  Promises of artificial intelligence in neuroradiology: a systematic technographic review.

Authors:  Allard W Olthof; Peter M A van Ooijen; Mohammad H Rezazade Mehrizi
Journal:  Neuroradiology       Date:  2020-04-22       Impact factor: 2.804

6.  AI-RADS: An Artificial Intelligence Curriculum for Residents.

Authors:  Alexander L Lindqwister; Saeed Hassanpour; Petra J Lewis; Jessica M Sin
Journal:  Acad Radiol       Date:  2020-10-15       Impact factor: 3.173

7.  Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19.

Authors:  Matthew D Li; Nishanth T Arun; Mehak Aggarwal; Sharut Gupta; Praveer Singh; Brent P Little; Dexter P Mendoza; Gustavo C A Corradi; Marcelo S Takahashi; Suely F Ferraciolli; Marc D Succi; Min Lang; Bernardo C Bizzo; Ittai Dayan; Felipe C Kitamura; Jayashree Kalpathy-Cramer
Journal:  Medicine (Baltimore)       Date:  2022-07-22       Impact factor: 1.817

Review 8.  The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging.

Authors:  Andrew M Taylor
Journal:  Pediatr Radiol       Date:  2021-12-22
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.