Publication Name	Artificial Intelligence in Medical Imaging
Manuscript ID	75227
Country	India

Received	2022-01-19 04:15
Peer-Review Started	2022-01-19 04:16
To Make the First Decision
Return for Revision	2022-03-12 03:00
Revised	2022-04-12 15:01
Second Decision	2022-06-08 03:12
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2022-06-17 23:01
Articles in Press	2022-06-17 23:01
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2022-06-26 15:58
Publish the Manuscript Online	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/
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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
Manuscript Source	Invited Manuscript
All Author List	Prakash Nadkarni and Suleman Adam Merchant
Funding Agency and Grant Number
Corresponding Author	Suleman Adam Merchant, MD, 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

Full Article (PDF)	AIMI-3-55.pdf
Full Article (Word)	AIMI-3-55.docx
Manuscript File	75227_Auto_Edited-LJH-LS.docx
Answering Reviewers	75227-Answering reviewers.pdf
Audio Core Tip	75227-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	75227-Conflict-of-interest statement.pdf
Copyright License Agreement	75227-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate	75227-Language certificate.pdf
Peer-review Report	75227-Peer-review(s).pdf
Scientific Misconduct Check	75227-Bing-Liu JH-2.jpg
Scientific Misconduct Check	75227-CrossCheck.jpg
Scientific Editor Work List	75227-Scientific editor work list.pdf