Big Data And Artificial Intelligence In Ophthalmology Clinical Application And Future Exploration

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

This book provides a wide-ranging overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms in ophthalmology. Expertly written chapters examine AI in age-related macular degeneration, glaucoma, retinopathy of prematurity and diabetic retinopathy screening. AI perspectives, systems and limitations are all carefully assessed throughout the book as well as the technical aspects of DL systems for retinal diseases including the application of Google DeepMind, the Singapore algorithm, and the Johns Hopkins algorithm. Artificial Intelligence in Ophthalmology meets the need for a resource that reviews the benefits and pitfalls of AI, ML and DL in ophthalmology. Ophthalmologists, optometrists, eye-care workers, neurologists, cardiologists, internal medicine specialists, AI engineers and IT specialists with an interest in how AI can help with early diagnosis and monitoring treatment in ophthalmic patients will find this book to be an indispensable guide to an evolving area of healthcare technology.

The book helps to explore the vast expanse of artificial intelligence-based scientific content that has been published in the last few years. Ophthalmology has recently undergone a silent digital revolution, with machine learning and deep learning algorithms consistently outperforming human graders in studies published across the globe. It is high time that a resource that breaks this information behemoth into easily digestible bits comes to the fore. This book simplifies the complex mechanics of algorithms used in ophthalmology and vision science applications. It also tries to address potential ethical issues with machines entering our clinics and patients’ lives. Overall it is essential reading for ophthalmologists/eye care professionals interested in artificial intelligence and everyone who is looking for a deep dive into the exciting world of digital medicine.

Current and Future Application of Artificial Intelligence in Clinical Medicine presents updates on the application of machine learning and deep learning techniques in medical procedures. . Chapters in the volume have been written by outstanding contributors from cancer and computer science institutes with the goal of providing updated knowledge to the reader. Topics covered in the book include 1) Artificial Intelligence (AI) applications in cancer diagnosis and therapy, 2) Updates in AI applications in the medical industry, 3) the use of AI in studying the COVID-19 pandemic in China, 4) AI applications in clinical oncology (including AI-based mining for pulmonary nodules and the use of AI in understanding specific carcinomas), 5) AI in medical imaging. Each chapter presents information on related sub topics in a reader friendly format. The combination of expert knowledge and multidisciplinary approaches highlighted in the book make it a valuable source of information for physicians and clinical researchers active in the field of cancer diagnosis and treatment (oncologists, oncologic surgeons, radiation oncologists, nuclear medicine physicians, and radiologists) and computer science scholars seeking to understand medical applications of artificial intelligence.

The pace and sophistication of advances in medicine in the past two decades have necessitated a growing need for a comprehensive reference that highlights current issues in medicine. Each volume in the Current Issues in Medicine series is a stand‐alone text that provides a broad survey of various critical topics—all accomplished in a user-friendly yet interconnected format. The series not only highlights current advances but also explores related topics such as translational medicine, regulatory science, neglected diseases, global pandemics, patent law, immunotoxicology, theranostics, big data, artificial intelligence, novel imaging tools, combination drug products, and novel therapies. While bridging the gap between basic research and clinical medicine, this series provides a thorough understanding of medicine’s potential to address health problems from both the patient’s and the provider’s perspectives in a healthcare setting. The range of topics covered and the expertise of the contributing authors accurately reflect the rapidly evolving areas within medicine—from basic medical sciences to clinical specialties. Each volume is essential reading for physicians, medical students, nurses, fellows, residents, undergraduate and graduate students, educators, policymakers, and biomedical researchers. The multidisciplinary approach of the series makes it a valuable reference resource for the pharmaceutical industry, academia, and governments. However, unlike other series on medicine or medical textbooks, this series focuses on current trends, perspectives, and issues in medicine that are central to healthcare delivery in the 21st century. Volume 2 focuses on the current issues in basic medical sciences, subjects that are fundamental to the practice of medicine. Specifically, it discusses clinical immunology, medical microbiology, COVID-19, and big data. These subjects, traditionally taught in the first two years of medical school that precede clinical instruction, provide a core of basic knowledge critical to the success in clinical medicine during rotations, training, and medical practice.