Artificial Intelligence and Machine Learning for Healthcare : Vol. 2: Emerging Methodologies and Trends /

In line with advances in digital and computing systems, artificial intelligence (AI) and machine learning (ML) technologies have transformed many aspects of medical and healthcare services, delivering tangible benefits to patents and the general public. This book is a sequel of the edition on "...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Lim, Chee Peng (Editor), Vaidya, Ashlesha (Editor), Chen, Yen-Wei (Editor), Jain, Vaishnavi (Editor), Jain, Lakhmi C. (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2023.
Edition:1st ed. 2023.
Series:Intelligent Systems Reference Library, 229
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary:In line with advances in digital and computing systems, artificial intelligence (AI) and machine learning (ML) technologies have transformed many aspects of medical and healthcare services, delivering tangible benefits to patents and the general public. This book is a sequel of the edition on "Artificial Intelligence and Machine Learning for Healthcare". The first volume is focused on utilization of AI and ML for image and data analytics in the medical and healthcare domains. In this second volume, emerging methodologies and future trends in AI and ML for advancing medical treatments and healthcare services are presented. The selected studies in this book provide readers a glimpse on current progresses in AI and ML for undertaking a variety of healthcare-related tasks. The advances in AI and ML technologies for future healthcare are also discussed, shedding light on the potential of AI and ML to realize the next-generation medical treatments and healthcare services for the betterment of our global society.
Physical Description:1 online resource (XIII, 273 pages 76 illustrations, 59 illustrations in color)
ISBN:9783031111709
ISSN:1868-4408 ;
DOI:10.1007/978-3-031-11170-9