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

Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare. Authors Kerrie Holley and Manish Mathur from Google's Healthcare and Life Sciences Industry team help you explore real-world applications of these technologies in healthcare, from personalized patient care and drug discovery to enhanced medical imaging and robot-assisted surgeries. You'll also learn the challenges of using these technologies--and the ethical implications of their application in this field. With this book, you will: Learn how LLMs and generative AI can help address and transform healthcare issues Explore the basics of LLMs and generative AI and learn how they work Learn how these technologies are being applied in healthcare today Understand several LLM and generative AI use cases Examine the ethics and challenges of applying LLMs and generative AI to healthcare Understand the potential use of LLMs and generative AI in healthcare in the near term and their prospects for the future

In recent years, generative artificial intelligence (AI) has emerged as a powerful tool with transformative potential across various industries, including healthcare. The convergence of advanced machine learning techniques, abundant healthcare data, and innovative algorithms has paved the way for generative AI to revolutionize medical research, diagnosis, treatment, and patient care. "Generative AI for Healthcare" is a comprehensive guide that explores the intersection of generative AI and healthcare, providing insights into the latest developments, applications, challenges, and future directions in this rapidly evolving field. From synthesizing medical images and generating electronic health records to personalized medicine and drug discovery, this book delves into the diverse ways in which generative AI is reshaping the landscape of healthcare delivery and biomedical research. Drawing upon expertise from interdisciplinary domains such as computer science, medicine, bioinformatics, and data science, "Generative AI for Healthcare" offers a holistic perspective on the potential and pitfalls of leveraging generative AI in healthcare. Through real-world case studies, practical examples, and expert insights, readers will gain a deeper understanding of how generative AI technologies are being applied to address critical healthcare challenges, improve patient outcomes, and accelerate medical innovation. Whether you are a healthcare professional, researcher, data scientist, or AI enthusiast, this book serves as a valuable resource for navigating the complex intersection of generative AI and healthcare. By exploring cutting-edge techniques, emerging trends, and ethical considerations, "Generative AI for Healthcare" empowers readers to harness the power of generative AI to drive positive change and innovation in healthcare delivery, ultimately advancing the future of medicine and improving human health worldwide.

AI is poised to transform every aspect of healthcare, including the way we manage personal health, from customer experience and clinical care to healthcare cost reductions. This practical book is one of the first to describe present and future use cases where AI can help solve pernicious healthcare problems. Kerrie Holley and Siupo Becker provide guidance to help informatics and healthcare leadership create AI strategy and implementation plans for healthcare. With this book, business stakeholders and practitioners will be able to build knowledge, a roadmap, and the confidence to support AIin their organizations—without getting into the weeds of algorithms or open source frameworks. Cowritten by an AI technologist and a medical doctor who leverages AI to solve healthcare’s most difficult challenges, this book covers: The myths and realities of AI, now and in the future Human-centered AI: what it is and how to make it possible Using various AI technologies to go beyond precision medicine How to deliver patient care using the IoT and ambient computing with AI How AI can help reduce waste in healthcare AI strategy and how to identify high-priority AI application

Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare. Authors Kerrie Holley, former Google healthcare professionals, guide you through the transformative potential of large language models (LLMs) and generative AI in healthcare. From personalized patient care and clinical decision support to drug discovery and public health applications, this comprehensive exploration covers real-world uses and future possibilities of LLMs and generative AI in healthcare. With this book, you will: Understand the promise and challenges of LLMs in healthcare Learn the inner workings of LLMs and generative AI Explore automation of healthcare use cases for improved operations and patient care using LLMs Dive into patient experiences and clinical decision-making using generative AI Review future applications in pharmaceutical R&D, public health, and genomics Understand ethical considerations and responsible development of LLMs in healthcare "The authors illustrate generative's impact on drug development, presenting real-world examples of its ability to accelerate processes and improve outcomes across the pharmaceutical industry."--Harsh Pandey, VP, Data Analytics & Business Insights, Medidata-Dassault Kerrie Holley is a retired Google tech executive, IBM Fellow, and VP/CTO at Cisco. Holley's extensive experience includes serving as the first Technology Fellow at United Health Group (UHG), Optum, where he focused on advancing and applying AI, deep learning, and natural language processing in healthcare. Manish Mathur brings over two decades of expertise at the crossroads of healthcare and technology. A former executive at Google and Johnson & Johnson, he now serves as an independent consultant and advisor. He guides payers, providers, and life sciences companies in crafting cutting-edge healthcare solutions.

The healthcare landscape is constantly evolving, and one of the most significant concerns that healthcare professionals deal with is understanding how to use biomedical intelligence to improve patient outcomes. With the increasing complexity of healthcare computing systems, including technologies like deep learning and the Internet of Things, it can be challenging to navigate these advancements. Machine Learning and Generative AI in Smart Healthcare is a practical tool for healthcare professionals, researchers, and policymakers who are seeking to implement biomedical intelligence solutions. It provides a clear roadmap for using prescriptive and predictive analytics in machine learning to enhance healthcare outcomes. Going beyond the basics, it delves into healthcare computing and networking complexities. By delving into topics such as data mining, disease prediction, and AI applications, deep learning approaches, decision support systems, and optimization techniques, this book equips readers with the practical knowledge they need to optimize healthcare delivery and management.

Generative AI in Healthcare: A Comprehensive Guide" explores the transformative role of artificial intelligence (AI) in revolutionizing healthcare practices. This book serves as a comprehensive resource for healthcare professionals, technologists, and enthusiasts interested in understanding how generative AI is reshaping the future of medicine. The book begins with an exploration of the foundational concepts of generative AI, providing insights into neural networks, deep learning fundamentals, and various generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer models. It delves into the importance of high-quality data and robust infrastructure necessary to support AI applications in healthcare settings. Part II of the book focuses on practical applications of generative AI in healthcare, including enhancing medical imaging accuracy, accelerating drug discovery processes, and personalizing treatment plans based on individual patient data. Real-world case studies and examples illustrate how AI is automating tasks, improving diagnostic accuracy, and supporting clinical decision-making. In Part III, the book addresses implementation challenges such as technical integration into existing healthcare systems, change management strategies, and ethical considerations around data privacy and algorithmic bias. It emphasizes the importance of training healthcare professionals to effectively leverage AI tools while adhering to regulatory standards. Part IV explores future directions of generative AI in healthcare, discussing emerging technologies like AI-enabled IoT devices, quantum computing applications, and advanced AI models. Global perspectives highlight international collaborations and case studies from different countries, showcasing diverse approaches to integrating AI in healthcare. Concluding with a forward-looking perspective, the book discusses long-term predictions for AI's role in healthcare, emphasizing its potential to enhance preventive care, improve patient outcomes, and streamline healthcare delivery.