株式会社極東書店トップ商品一覧AI in Modern Ophthalmology: Revolutionizing Eye Care with Machine Learning, and Deep Learning.

商品詳細

AI in Modern Ophthalmology: Revolutionizing Eye Care with Machine Learning, and Deep Learning.

AI in Modern Ophthalmology: Revolutionizing Eye Care with Machine Learning, and Deep Learning.

・ISBN 978-1-041-30474-6 hard GB£ 103.99

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お気に入り
著者・編者Komolafe, Temitope Emmanuel / Jeevakumari S, Jenifer / K, Manimala / Juliet S, Ebenezer / Williams Samuel, Oluwarotimi (eds.),
シリーズ (Analytics and AI for Healthcare)
出版社 (CRC Press, UK)
出版年月2027
ページ数212 pp.
言語ENG
ニュース番号<A05-88814>

解説

The book provides an authoritative and accessible guide on integrating intelligent technologies, specifically Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), into modern eye care.

Written by experts in ophthalmology, medical AI, and healthcare innovation, it offers a multidisciplinary exploration of how these tools are fundamentally redefining ophthalmic diagnostics, therapeutics, and surgical interventions. The core focus is on bridging the gap between technical AI innovations and their clinical deployment, offering a practical roadmap for clinicians and researchers. It combines cutting-edge research with real-world applications to demonstrate how AI is being used for the early and accurate detection of retinal diseases and glaucoma, enhancing surgical precision via robotics, and guiding personalized treatment plans. Three key areas are studied throughout the book. First, AI-powered imaging diagnostics, to explore how ML and DL models are used for the early and accurate detection of retinal diseases and glaucoma, supporting faster and more precise decision-making. Second, the application of AI-assisted surgical robotics in ophthalmology: In this topic, we shall examine how AI integration in robotic surgery enhances precision, reduces risk, and supports surgical planning. Third, ethical and regulatory considerations to address the responsible adoption of AI, including data governance, patient privacy, and health equity in ophthalmic care. These topics are essential for stakeholders navigating the clinical, technical, and ethical dimensions of AI implementation and adoption. Ultimately, the text aims to show how the responsible adoption of AI is actively improving clinical outcomes and reshaping the future of vision science.

The primary audience for this book includes students taking advanced undergraduate or postgraduate courses on medical technology, medical imaging and healthcare bioinformatics. It would also serve as a useful guide for practicing ophthalmologists, optometrists, biomedical engineers, computer vision experts, and AI developers, working in the healthcare and medical related areas.