AI for Radiology

個数:1
紙書籍版価格
¥42,267
  • 電子書籍
  • ポイントキャンペーン

AI for Radiology

  • 著者名:Marques, Oge
  • 価格 ¥5,627 (本体¥5,116)
  • CRC Press(2024/02/12発売)
  • 春うらら!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~3/15)
  • ポイント 1,530pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9780367627782
  • eISBN:9781003836445

ファイル: /

Description

Artificial intelligence (AI) has revolutionized many areas of medicine and is increasingly being embraced. This book focuses on the integral role of AI in radiology, shedding light on how this technology can enhance patient care and streamline professional workflows.

This book reviews, explains, and contextualizes some of the most current, practical, and relevant developments in artificial intelligence and deep learning in radiology and medical image analysis. AI for Radiology presents a balanced viewpoint of the impact of AI in these fields, underscoring that AI technologies are not intended to replace radiologists but rather to augment their capabilities, freeing professionals to focus on more complex cases. This book guides readers from the basic principles of AI to their practical applications in radiology, moving from the role of data in AI to the ethical and regulatory considerations of using AI in radiology and concluding with a selection of resources for further exploration.

This book has been crafted with a diverse readership in mind. It is a valuable asset for medical professionals eager to stay up to date with AI developments, computer scientists curious about AI窶冱 clinical applications, and anyone interested in the intersection of healthcare and technology.

Table of Contents

1 Artificial Intelligence and Medicine: The Big Picture

2 AI in Radiology: From Fear to Leadership

3 Fundamentals of Machine Learning and Deep Learning

4 Fundamentals of Medical Image Analysis

5 Data: The Essential Ingredient in AI Solutions

6 Clinical Applications of AI in Radiology

7 Harnessing AI in Radiology Education and Training

8 Getting Started with Deep Learning in Medical Imaging

9 The Future of AI in Radiology

10 Resources for Further Learning

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