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Articles Published Processes
6/28/2022 2:17:26 AM | Browse: 366 | Download: 803
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Received |
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2022-01-19 04:15 |
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Peer-Review Started |
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2022-01-19 04:16 |
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To Make the First Decision |
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Return for Revision |
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2022-03-12 03:00 |
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Revised |
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2022-04-12 15:01 |
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Second Decision |
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2022-06-08 03:12 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2022-06-17 23:01 |
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Articles in Press |
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2022-06-17 23:01 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2022-06-26 15:58 |
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Publish the Manuscript Online |
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2022-06-28 02:17 |
ISSN |
2644-3260 (online) |
Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
Copyright |
©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved. |
Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
Website |
http://www.wjgnet.com |
Category |
Radiology, Nuclear Medicine & Medical Imaging |
Manuscript Type |
Minireviews |
Article Title |
Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters: Future insights
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Manuscript Source |
Invited Manuscript |
All Author List |
Prakash Nadkarni and Suleman Adam Merchant |
ORCID |
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Funding Agency and Grant Number |
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Corresponding Author |
Suleman Adam Merchant, MD, Doctor, Doctor, Former Dean, Professor & Head ( Radiology ), LTM Medical College & LTM General Hospital, SION HOSPITAL, Mumbai 400022, Maharashtra, India. suleman.a.merchant@gmail.com |
Key Words |
Medical imaging; Artificial intelligence; Federated learning; holistic approach; Quantum computing; Future insights |
Core Tip |
It is necessary to understand the principles of how different artificial intelligence (AI) approaches work to appreciate their respective strengths and limitations. While advances in deep neural net research in Radiology are impressive, their focus must shift from applications that perform only single recognition task, to those that perform realistic multi-recognition tasks that radiologists perform daily. Humans use multiple problem-solving strategies, applying each as needed. Similarly, realistic AI solutions must combine multiple approaches. Good radiologists are also good clinicians. AI must similarly be able to use all available evidence, not imaging information alone, and not just one/Limited aspects of imaging. Both humans and computer algorithms (including AI) can be biased. A way to reduce bias, as well as prevent failure, is better explainability – the ability to clearly describe the workings of a particular application to a subject-matter expert unfamiliar with AI technology. Federated learning allows more generalizable and accurate machine-learning models to be created by preserving data privacy, concerns about which form a major barrier to large-scale collaboration. While the physical hurdles to implementing Quantum computing at a commercial level are formidable, this technology has the potential to revolutionize all of computing. |
Publish Date |
2022-06-28 02:17 |
Citation |
Nadkarni P, Merchant SA. Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters: Future insights. Artif Intell Med Imaging 2022; 3(3): 55-69 |
URL |
https://www.wjgnet.com/2644-3260/full/v3/i3/55.htm |
DOI |
https://dx.doi.org/10.35711/aimi.v3.i3.55 |
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