Literature DB >> 27652572

The End of Radiology? Three Threats to the Future Practice of Radiology.

Katie Chockley1, Ezekiel Emanuel2.   

Abstract

Radiology faces at least three major, potentially fatal, threats. First, as care moves out of the hospital, there will be a decrease in demand for imaging. More care in patients' homes and in other nonhospital settings means fewer medical tests, including imaging. Second, payment reform and, in particular, bundled payments and capitation mean that imaging will become a cost rather than a profit center. These shifts in provider payment will decrease the demand for imaging and disrupt the practice of radiology. Potentially, the ultimate threat to radiology is machine learning. Machine learning will become a powerful force in radiology in the next 5 to 10 years and could end radiology as a thriving specialty.
Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Machine learning; future of health care; payment reform; technology

Mesh:

Year:  2016        PMID: 27652572     DOI: 10.1016/j.jacr.2016.07.010

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


  22 in total

1.  Artificial intelligence in radiology: how will we be affected?

Authors:  S H Wong; H Al-Hasani; Z Alam; A Alam
Journal:  Eur Radiol       Date:  2018-07-19       Impact factor: 5.315

2.  AI Hype and Radiology: A Plea for Realism and Accuracy.

Authors:  John Banja
Journal:  Radiol Artif Intell       Date:  2020-07-01

3.  Artificial intelligence and radiomics in nuclear medicine: potentials and challenges.

Authors:  Cumali Aktolun
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12       Impact factor: 9.236

4.  Residents' Introduction to Comparative Effectiveness Research and Big Data Analytics.

Authors:  Stella K Kang; Christoph I Lee; Pari V Pandharipande; Pina C Sanelli; Michael P Recht
Journal:  J Am Coll Radiol       Date:  2017-01-27       Impact factor: 5.532

5.  Importance of Better Human-Computer Interaction in the Era of Deep Learning: Mammography Computer-Aided Diagnosis as a Use Case.

Authors:  Robert M Nishikawa; Kyongtae T Bae
Journal:  J Am Coll Radiol       Date:  2017-10-31       Impact factor: 5.532

6.  Will machine learning end the viability of radiology as a thriving medical specialty?

Authors:  Stephen Chan; Eliot L Siegel
Journal:  Br J Radiol       Date:  2018-11-01       Impact factor: 3.039

Review 7.  Computer-aided diagnosis in the era of deep learning.

Authors:  Heang-Ping Chan; Lubomir M Hadjiiski; Ravi K Samala
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

Review 8.  Artificial intelligence in dermatology and healthcare: An overview.

Authors:  Varadraj Vasant Pai; Rohini Bhat Pai
Journal:  Indian J Dermatol Venereol Leprol       Date:  2021 [SEASON]       Impact factor: 2.545

9.  Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors.

Authors:  Lea Strohm; Charisma Hehakaya; Erik R Ranschaert; Wouter P C Boon; Ellen H M Moors
Journal:  Eur Radiol       Date:  2020-05-26       Impact factor: 5.315

10.  Attitudes towards medical artificial intelligence talent cultivation: an online survey study.

Authors:  Dongyuan Yun; Yifan Xiang; Zhenzhen Liu; Duoru Lin; Lanqin Zhao; Chong Guo; Peichen Xie; Haotian Lin; Yizhi Liu; Yuxian Zou; Xiaohang Wu
Journal:  Ann Transl Med       Date:  2020-06
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