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| a guide to artificial intelligence in healthcare: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 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 |
| a guide to artificial intelligence in healthcare: AI in Health Tom Lawry, 2020-02-05 We are in the early stages of the next big platform shift in healthcare computing. Fueled by Artificial Intelligence (AI) and the Cloud, this shift is already transforming the way health and medical services are provided. As the industry transitions from static digital repositories to intelligent systems, there will be winners and losers in the race to innovate and automate the provision of services. Critical to success will be the role leaders play in shaping the use of AI to be less artificial and more intelligent in support of improving processes to deliver care and keep people healthy and productive across all care settings. This book defines key technical, process, people, and ethical issues that need to be understood and addressed in successfully planning and executing an enterprise-wide AI plan. It provides clinical and business leaders with a framework for moving organizations from the aspiration to execution of intelligent systems to improve clinical, operational, and financial performance. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Medicine David Riaño, Szymon Wilk, Annette ten Teije, 2019-06-19 This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning. |
| a guide to artificial intelligence in healthcare: An Introductory Guide to Artificial Intelligence for Legal Professionals Juan Pavón, María Jesús González-Espejo, 2020-05-14 The availability of very large data sets and the increase in computing power to process them has led to a renewed intensity in corporate and governmental use of Artificial Intelligence (AI) technologies. This groundbreaking book, the first devoted entirely to the growing presence of AI in the legal profession, responds to the necessity of building up a discipline that due to its novelty requires the pooling of knowledge and experiences of well-respected experts in the AI field, taking into account the impact of AI on the law and legal practice. Essays by internationally known expert authors introduce the essentials of AI in a straightforward and intelligible style, offering jurists as many practical examples and business cases as possible so that they are able to understand the real application of this technology and its impact on their jobs and lives. Elements of the analysis include the following: crucial terms: natural language processing, machine learning and deep learning; regulations in force in major jurisdictions; ethical and social issues; labour and employment issues, including the impact that robots have on employment; prediction of outcome in the legal field (judicial proceedings, patent granting, etc.); massive analysis of documents and identification of patterns from which to derive conclusions; AI and taxation; issues of competition and intellectual property; liability and responsibility of intelligent systems; AI and cybersecurity; AI and data protection; impact on state tax revenues; use of autonomous killer robots in the military; challenges related to privacy; the need to embrace transparency and sustainability; pressure brought by clients on prices; minority languages and AI; danger that the existing gap between large and small businesses will further increase; how to avoid algorithmic biases when AI decides; AI application to due diligence; AI and non-disclosure agreements; and the role of chatbots. Interviews with pioneers in the field are included, so readers get insights into the issues that people are dealing with in day-to-day actualities. Whether conceiving AI as a transformative technology of the labour market and training or an economic and business sector in need of legal advice, this introduction to AI will help practitioners in tax law, labour law, competition law and intellectual property law understand what AI is, what it serves, what is the state of the art and the potential of this technology, how they can benefit from its advantages and what are the risks it presents. As the global economy continues to suffer the repercussions of a framework that was previously fundamentally self-regulatory, policymakers will recognize the urgent need to formulate rules to properly manage the future of AI. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Healthcare Parag Mahajan, 2021-02 ① Do you know what AI is doing to improve our health and wellbeing? ② Does this new technology concern you, or impress you? ③ Do you want to know more about the future of AI in healthcare? Technology continues to advance at a pace that can seem bewildering. Nowhere else is it moving faster than in the health sector, where ♥AI is now being used to improve millions of lives♥. In this book, ◆ Artificial Intelligence in Healthcare: AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone ◆, you can discover the great improvements that AI is making, with chapters covering: The current applications and future of AI in healthcare and all major medical specialties ✓ The benefits and risks weighed up ✓ The ethics involved ✓ Machine learning and data science simplified ✓ AI's role in medical research and education, health insurance, drug discovery, electronic health records, and the fight against COVID-19 ✓ The roles that major corporations and start-up companies are playing ✓ The implementation of AI in clinical practice ✓ And lots more... Quite simply the most authoritative text on the subject, Artificial Intelligence in Healthcare - 3rd Edition, is an absorbing and compelling read for anyone who wants to know more. It is packed with more updated information than any other book currently available, written in easy-to-understand language, and accessible to all. |
| a guide to artificial intelligence in healthcare: Machine Learning and AI for Healthcare Arjun Panesar, 2019-02-04 Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Medicine Niklas Lidströmer, Hutan Ashrafian, 2022-03-17 This book provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting. Artificial Intelligence in Medicine aims to give readers the required knowledge to apply artificial intelligence to clinical practice. The book is relevant to medical students, specialist doctors, and researchers whose work will be affected by artificial intelligence. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Medical Imaging Erik R. Ranschaert, Sergey Morozov, Paul R. Algra, 2019-01-29 This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals. |
| a guide to artificial intelligence in healthcare: Deep Medicine Eric Topol, 2019-03-12 A Science Friday pick for book of the year, 2019 One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Healthcare Parag Suresh Mahajan MD, 2018-07 About the book - Artificial Intelligence in Healthcare Do you want to know the relationship between Artificial Intelligence (AI) & healthcare, & how AI is improving healthcare? Technology is evolving rapidly, & you need to keep up to stay at the top. Artificial Intelligence (AI) is revolutionizing all aspects of healthcare & this book is intended to be your companion on this journey. It's a power-packed AI book that guides you about the current state and future applications of AI in healthcare, including those under development, in a simple to understand language. It discusses the ethical concerns related to the use of AI in healthcare, principles of AI & how it works, the vital role of AI in all major medical specialties and health insurance, & the role of start-ups and corporate players in AI in healthcare. About the Author - Dr Parag Suresh Mahajan MD Dr. Parag Mahajan is an Accomplished Entrepreneurial CEO of multiple Healthcare Start-ups, Radiologist, Clinical Informatician, Teacher, Researcher, and Author. His current interests include the development of Start-ups in the fields of Artificial Intelligence in Healthcare, Blockchain in Healthcare, Electronic Health Records, and Medical eLearning Systems. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Healthcare Lalit Garg, Sebastian Basterrech, Chitresh Banerjee, Tarun K. Sharma, 2021-10-29 This book highlights the analytics and optimization issues in healthcare systems, proposes new approaches, and presents applications of innovative approaches in real facilities. In the past few decades, there has been an exponential rise in the application of swarm intelligence techniques for solving complex and intricate problems arising in healthcare. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. The primary objective of this book is to bring forward thorough, in-depth, and well-focused developments of hybrid variants of swarm intelligence algorithms and their applications in healthcare systems. |
| a guide to artificial intelligence in healthcare: Advanced Introduction to Artificial Intelligence in Healthcare Davenport, Tom, Glaser, John, Gardner, Elizabeth, 2022-08-05 Providing a comprehensive overview of the current and future uses of Artificial Intelligence in healthcare, this Advanced Introduction discusses the issues surrounding the implementation, governance, impacts and risks of utilising AI in health organizations. Analysing AI technologies in healthcare and their impacts on patient care, medical devices, pharmaceuticals, population health, and healthcare operations, it advises healthcare executives on how to effectively leverage AI to advance their strategies to support digital transformation. |
| a guide to artificial intelligence in healthcare: AI-First Healthcare Kerrie L. Holley, Siupo Becker M.D., 2021-04-19 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 |
| a guide to artificial intelligence in healthcare: Primary Care Barbara Starfield, 1992 This comprehensive work provides a lucid examination of the difficult problems that arise with the implementation of effective primary care. The book has four purposes: to help practitioners of primary care understand what they do and why; to provide a basis for the training of primary care practitioners; to stimulate research that will provide a more substantive basis for improvements in primary care; and to help policy makers understand the difficulties and challenges of primary care and its importance. In addition to discussing systems of primary care and alternative ways of evaluating them, the author addresses important issues such as practitioner-patient communication, information systems and medical records, referral processes, personnel, managed care, financing, quality assessment and community orientation. This unique volume provides a clear and valuable assessment of the basic concepts, issues and challenges in this increasingly important field. |
| a guide to artificial intelligence in healthcare: Transforming Healthcare with Big Data and AI Alex Liu, Anna Farzindar, Mingbo Gong, 2020-04-01 Healthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field. This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare. |
| a guide to artificial intelligence in healthcare: Multiple Perspectives on Artificial Intelligence in Healthcare Mowafa Househ, Elizabeth Borycki, Andre Kushniruk, 2021-08-05 This book offers a comprehensive yet concise overview of the challenges and opportunities presented by the use of artificial intelligence in healthcare. It does so by approaching the topic from multiple perspectives, e.g. the nursing, consumer, medical practitioner, healthcare manager, and data analyst perspective. It covers human factors research, discusses patient safety issues, and addresses ethical challenges, as well as important policy issues. By reporting on cutting-edge research and hands-on experience, the book offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes. It will also benefit students and researchers whose work involves artificial intelligence-related research issues in healthcare. |
| a guide to artificial intelligence in healthcare: You and AI Rajeev Ronanki, 2021-11-09 THE HEALTHCARE INDUSTRY HAS PROBLEMS. EXPONENTIAL TECHNOLOGIES ARE THE ANWSER. Exponential technologies are transforming our present and shaping the future. The leading companies of today--and tomorrow--are those that make innovative exponential technologies central to their business practices and strategy. In You and AI, author Rajeev Ronanki explores how exponential technologies can solve the problems that plague the American healthcare industry and provide everyone better, personalized healthcare at lower cost. Enterprising healthcare companies like Anthem are forging the way by integrating exponential technologies right into the company DNA, creating AI-first, blockchain-first, data-first, technology-first digital enterprises for the twenty-first century and beyond. |
| a guide to artificial intelligence in healthcare: Handbook of Artificial Intelligence in Healthcare Chee-Peng Lim, Ashlesha Vaidya, Kiran Jain, Virag U. Mahorkar, Lakhmi C. Jain, 2022 This handbook on Artificial Intelligence (AI) in healthcare consists of two volumes. The first volume is dedicated to advances and applications of AI methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context. The advent of digital and computing technologies has created a surge in the development of AI methodologies and their penetration to a variety of activities in our daily lives in recent years. Indeed, researchers and practitioners have designed and developed a variety of AI-based systems to help advance health and well-being of humans. In this first volume, we present a number of latest studies in AI-based tools and techniques from two broad categories, viz., medical signal, image, and video processing as well as healthcare information and data analytics in Part 1 and Part 2, respectively. These selected studies offer readers practical knowledge and understanding pertaining to the recent advances and applications of AI in the healthcare sector. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence and Machine Learning in Health Care and Medical Sciences Gyorgy J. Simon, |
| a guide to artificial intelligence in healthcare: Intelligent Decision Support Systems—A Journey to Smarter Healthcare Smaranda Belciug, Florin Gorunescu, 2019-03-20 The goal of this book is to provide, in a friendly and refreshing manner, both theoretical concepts and practical techniques for the important and exciting field of Artificial Intelligence that can be directly applied to real-world healthcare problems. Healthcare – the final frontier. Lately, it seems like Pandora opened the box and evil was released into the world. Fortunately, there was one thing left in the box: hope. In recent decades, hope has been increasingly represented by Intelligent Decision Support Systems. Their continuing mission: to explore strange new diseases, to seek out new treatments and drugs, and to intelligently manage healthcare resources and patients. Hence, this book is designed for all those who wish to learn how to explore, analyze and find new solutions for the most challenging domain of all time: healthcare. |
| a guide to artificial intelligence in healthcare: Smart Healthcare Systems Adwitiya Sinha, Megha Rathi, 2019-07-24 About the Book The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing. Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trends Target Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence and Machine Learning in Healthcare Ankur Saxena, Shivani Chandra, 2021-05-06 This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare. |
| a guide to artificial intelligence in healthcare: Introduction to Deep Learning for Healthcare Cao Xiao, Jimeng Sun, 2021-11-11 This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’ increasing use. The authors present deep learning case studies on all data described. Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching. This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research. |
| a guide to artificial intelligence in healthcare: Enterprise Artificial Intelligence Transformation Rashed Haq, 2020-06-10 Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation. |
| a guide to artificial intelligence in healthcare: Future of Health Technology Renata Glowacka Bushko, 2002 This text provides a comprehensive vision of the future of health technology by looking at the ways to advance medical technologies, health information infrastructure and intellectual leadership. It also explores technology creations, adoption processes and the impact of evolving technologies. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Medical Imaging Lia Morra, Silvia Delsanto, Loredana Correale, 2019-11-25 Choice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective |
| a guide to artificial intelligence in healthcare: Intelligence-Based Medicine Anthony C. Chang, 2020-06-27 Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. - Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything - Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists - Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future - Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare |
| a guide to artificial intelligence in healthcare: Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare Mark Chang, 2020-05-12 Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer science’s use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: · Covers broad AI topics in drug development, precision medicine, and healthcare. · Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. · Introduces the similarity principle and related AI methods for both big and small data problems. · Offers a balance of statistical and algorithm-based approaches to AI. · Provides examples and real-world applications with hands-on R code. · Suggests the path forward for AI in medicine and artificial general intelligence. As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence and Internet of Things Lalit Mohan Goyal, Tanzila Saba, Amjad Rehman, Souad Larabi-Marie-Sainte, 2021-08-25 This book reveals the applications of AI and IoT in smart healthcare and medical systems. It provides core principles, algorithms, protocols, emerging trends, security problems, and the latest e-healthcare services findings. The book also provides case studies and discusses how AI and IoT applications such as wireless devices, sensors, and deep learning could play a major role in assisting patients, doctors, and pharmaceutical staff. It focuses on how to use AI and IoT to keep patients safe and healthy and, at the same time, empower physicians to deliver superlative care. This book is written for researchers and practitioners working in the information technology, computer science, and medical equipment manufacturing industry for products and services having basic- and high-level AI and IoT applications. The book is also a useful guide for academic researchers and students. |
| a guide to artificial intelligence in healthcare: Measuring Quality Improvement in Healthcare Raymond G. Carey, Robert C. Lloyd, 2001-09-25 This ground-breaking book addresses the critical, growing need among health care administrators and practitioners to measure the effectiveness of quality improvement efforts. Written by respected healthcare quality professionals, Measuring Quality Improvement in Healthcare covers practical applications of the tools and techniques of statistical process control (SPC), including control charts, in healthcare settings. The authors' straightforward discussions of data collection, variation, and process improvement set the context for the use and interpretation of control charts. Their approach incorporates the voice of the customer as a key element driving the improvement processes and outcomes. The core of the book is a set of 12 case studies that show how to apply statistical thinking to health care process, and when and how to use different types of control charts. The practical, down-to-earth orientation of the book makes it accessible to a wide readership. Only authors who have used statistics and control charts to solve real-world healthcare problems could have written a book so practical and timely. - Barry S. Bader, Publisher The Quality Letter for Healthcare Leaders Many clinicians and other healthcare leaders underestimate the great contributions that better statistical thinking could make toward reducing costs and improving outcomes. This fascinating and timely book is a fine guide for getting started. - Donald M. Berwick, M.D. President and CEO, Institute for Healthcare Improvement Associate Professor of Pediatrics, Harvard Medical School Contents: Planning Your CQI Journey, Preparing to Collect Data, Data Collection, Understanding Variation, Using Run and Control Charts to Analyze Process Variation, Control Chart Case Studies, Developing Improvement Strategies, Using Patient Surveys for CQI, Formulas for Calculating Control Limits |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Behavioral and Mental Health Care David D. Luxton, 2015-09-10 Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. - Summarizes AI advances for use in mental health practice - Includes advances in AI based decision-making and consultation - Describes AI applications for assessment and treatment - Details AI advances in robots for clinical settings - Provides empirical data on clinical efficacy - Explores practical issues of use in clinical settings |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Surgery: Understanding the Role of AI in Surgical Practice Daniel A. Hashimoto, Guy Rosman, Ozanan R. Meireles, 2021-03-08 Build a solid foundation in surgical AI with this engaging, comprehensive guide for AI novices Machine learning, neural networks, and computer vision in surgical education, practice, and research will soon be de rigueur. Written for surgeons without a background in math or computer science, Artificial Intelligence in Surgery provides everything you need to evaluate new technologies and make the right decisions about bringing AI into your practice. Comprehensive and easy to understand, this first-of-its-kind resource illustrates the use of AI in surgery through real-life examples. It covers the issues most relevant to your practice, including: Neural Networks and Deep Learning Natural Language Processing Computer Vision Surgical Education and Simulation Preoperative Risk Stratification Intraoperative Video Analysis OR Black Box and Tracking of Intraoperative Events Artificial Intelligence and Robotic Surgery Natural Language Processing for Clinical Documentation Leveraging Artificial Intelligence in the EMR Ethical Implications of Artificial Intelligence in Surgery Artificial Intelligence and Health Policy Assessing Strengths and Weaknesses of Artificial Intelligence Research Finally, the appendix includes a detailed glossary of terms and important learning resources and techniques―all of which helps you interpret claims made by studies or companies using AI. |
| a guide to artificial intelligence in healthcare: Guide to Health Informatics Enrico Coiera, 2015-03-06 This essential text provides a readable yet sophisticated overview of the basic concepts of information technologies as they apply in healthcare. Spanning areas as diverse as the electronic medical record, searching, protocols, and communications as well as the Internet, Enrico Coiera has succeeded in making this vast and complex area accessible and understandable to the non-specialist, while providing everything that students of medical informatics need to know to accompany their course. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Healthcare and Medicine Kayvan Najarian, Delaram Kahrobaei, Enrique Dominguez, Reza Soroushmehr, 2022-04-06 This book provides a comprehensive overview of the recent developments in clinical decision support systems, precision health, and data science in medicine. The book targets clinical researchers and computational scientists seeking to understand the recent advances of artificial intelligence (AI) in health and medicine. Since AI and its applications are believed to have the potential to revolutionize healthcare and medicine, there is a clear need to explore and investigate the state-of-the-art advancements in the field. This book provides a detailed description of the advancements, challenges, and opportunities of using AI in medical and health applications. Over 10 case studies are included in the book that cover topics related to biomedical image processing, machine learning for healthcare, clinical decision support systems, visualization of high dimensional data, data security and privacy, bioinformatics, and biometrics. The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text. Companies in the healthcare sector can greatly benefit from the case studies covered in the book. Moreover, this book also: Provides an overview of the recent developments in clinical decision support systems, precision health, and data science in medicine Examines the advancements, challenges, and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Kayvan Najarian is a Professor in the Department of Computational Medicine and Bioinformatics, Department of Electrical Engineering and Computer Science, and Department of Emergency Medicine at the University of Michigan, Ann Arbor. Delaram Kahrobaei is the University Dean for Research at City University of New York (CUNY), a Professor of Computer Science and Mathematics, Queens College CUNY, and the former Chair of Cyber Security, University of York. Enrique Domínguez is a professor in the Department of Computer Science at the University of Malaga and a member of the Biomedical Research Institute of Malaga. Reza Soroushmehr is a Research Assistant Professor in the Department of Computational Medicine and Bioinformatics and a member of the Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor. |
| a guide to artificial intelligence in healthcare: The Cambridge Handbook of Artificial Intelligence Keith Frankish, William M. Ramsey, 2014-06-12 An authoritative, up-to-date survey of the state of the art in artificial intelligence, written for non-specialists. |
| a guide to artificial intelligence in healthcare: Artificial Intelligence in Medicine Lei Xing, Maryellen L. Giger, James K. Min, 2020-09-03 Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. - Provides history and overview of artificial intelligence, as narrated by pioneers in the field - Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence - Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach |
| a guide to artificial intelligence in healthcare: Precision Medicine and Artificial Intelligence Michael Mahler, 2021-03-12 Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine |
| a guide to artificial intelligence in healthcare: Computer Vision In Medical Imaging Chi Hau Chen, 2013-11-18 The major progress in computer vision allows us to make extensive use of medical imaging data to provide us better diagnosis, treatment and predication of diseases. Computer vision can exploit texture, shape, contour and prior knowledge along with contextual information from image sequence and provide 3D and 4D information that helps with better human understanding. Many powerful tools have been available through image segmentation, machine learning, pattern classification, tracking, reconstruction to bring much needed quantitative information not easily available by trained human specialists. The aim of the book is for both medical imaging professionals to acquire and interpret the data, and computer vision professionals to provide enhanced medical information by using computer vision techniques. The final objective is to benefit the patients without adding to the already high medical costs. |
| a guide to artificial intelligence in healthcare: Accelerated Path to Cures Josep Bassaganya-Riera, 2018 Accelerated Path to Cures provides a transformative perspective on the power of combining advanced computational technologies, modeling, bioinformatics and machine learning approaches with nonclinical and clinical experimentation to accelerate drug development. This book discusses the application of advanced modeling technologies, from target identification and validation to nonclinical studies in animals to Phase 1-3 human clinical trials and post-approval monitoring, as alternative models of drug development. As a case of successful integration of computational modeling and drug development, we discuss the development of oral small molecule therapeutics for inflammatory bowel disease, from the application of docking studies to screening new chemical entities to the development of next-generation in silico human clinical trials from large-scale clinical data. Additionally, this book illustrates how modeling techniques, machine learning, and informatics can be utilized effectively at each stage of drug development to advance the progress towards predictive, preventive, personalized, precision medicine, and thus provide a successful framework for Path to Cures. |
| a guide to artificial intelligence in healthcare: Digital Future of Healthcare Nilanjan Dey, Nabanita Das, Jyotismita Chaki, 2024-10-07 This book focusses on applications of different digital platforms in the field of healthcare including different devices used, their benefits, diagnosis, use in treatment, and use cases related to mobile healthcare. It covers machine learning, blockchain technology, big data analytics digital healthcare, telehealth technology and so forth. |
A GUIDE TO ARTIFICIAL INTELLIGENCE IN HEALTHCARE - CA …
A GUIDE TO ARTIFICIAL INTELLIGENCE IN HEALTHCARE. Copyright: The Medical Futurist 2019. CONTENTS. Part I. THE BASICS OF ARTIFICIAL INTELLIGENCE. Artificial …
Principles for Artificial Intelligence (AI) and its application in ...
Implementing AI in healthcare requires careful consideration beyond merely introducing new technologies. Key issues include ensuring AI tools are rigorously tested for safety and eficacy, …
Artificial Intelligence
What is AI? A primer for clinicians. Patient safety. The doctor and patient relationship. Public acceptance and trust. Accountability for decisions. Bias, inequality and unfairness. Data …
Artificial Intelligence In Health Care - Massachusetts Institute of ...
Understand the potential for AI to transform health care, from disease diagnosis to hospital optimization and patient care. Learn how adopting an integrated approach to AI can improve …
Artificial intelligence in healthcare: An essential guide for health ...
This article provides a guide to understand the fundamentals of AI technologies (ie, machine learning, natural language processing, and AI voice assistants) as well as their proper use in …
Artificial Intelligence in Healthcare - AMA
Artificial Intelligence in Healthcare. This document outlines the AMA’s position on the application of artificial intelligence (AI) and AI tools in healthcare. AI tools for the purpose of this position …
AI Doctor: The Rise of Artificial Intelligence in Healthcare: A Guide ...
Section II (“Applications of AI in Healthcare”) examines in great detail the opportunities for AI health care applications in diagnostics, therapeu-tics, decision support, population health, …
Transforming healthcare with AI - McKinsey & Company
Artificial intelligence (AI) has the potential to transform how care is delivered. It can support improvements in care outcomes, patient experience and access to healthcare services. It can …
Using Artificial Intelligence to Support Healthcare Decisions
In healthcare, AI has made advances in analysing data about how diseases progress. It is also being used to identify molecules that could make new drugs, diagnose medical conditions …
Artificial Intelligence in Healthcare - Executives for Health …
Intelligence affords the ability to spot trends and patterns across certain groups, monitor overall plan performance across populations and take advantage of a variety of data types, such as …
BLUEPRINT FOR TRUSTWORTHY AI IMPLEMENTATION GUIDANCE …
The use of artificial intelligence (AI) in healthcare offers enormous potential for accelerating clinical research and improving the quality and efficiency of healthcare delivery. However, a …
Artificial Intelligence: How to get it right
Artificial Intelligence (AI) has the potential to make a significant difference to health and care. A broad range of techniques can be used to create Artificially Intelligent Systems (AIS) to carry …
2024 Artificial Intelligence for Health - World Health Organization
We develop standards, policies, and guidance to guide countries on safe and ethical use of AI in health. We enable countries to establish safe and equitable AI ecosystems in health. We …
Artificial Intelligence and Primary Care - Royal College of General ...
This paper discusses and provides a brief overview of the role and potential impact that artificial intelligence (AI) may have in primary care. It outlines the basic principles of AI, summarising …
Ethics and governance of artificial intelligence for health
The original WHO guidance on ethics and governance of AI for health (1) examined various approaches to machine learning and various applications of AI in health care but did not …
AI Doctor: The Rise of Artificial Intelligence in Healthcare: A Guide ...
see artificial intelligence AI adoption in healthcare, drivers of availability of data, 92–95 inefficient care pathways, 106–109 key drivers of, 92 macroeconomic drivers, 91 non‐personalized care, …
Artificial Intelligence in Healthcare: 10 Questions
By applying advanced analytics and artificial intelligence (AI) to data, healthcare providers can identify insights and patterns that enhance clinical, operational, and financial decision-making. …
Artificial Intelligence (AI) Strategy - HHS.gov
HHS’ regulatory responsibility spans all aspects of healthcare including standards for healthcare delivery, payments, medical device software, medical products and food, and privacy to …
Understanding healthcare workers confidence in AI
influencing healthcare workers’ confidence in artificial intelligence (AI) technologies and how these can inform the development of related education and training. The research follows the Topol …
A National Policy Roadmap for Artificial Intelligence in Healthcare
A fully funded national plan by 2025 designed to create an AI-enabled Australian healthcare system capable of delivering personalised healthcare, safely, ethically and sustainably …
A GUIDE TO ARTIFICIAL INTELLIGENCE IN HEALTHCARE - CA …
A GUIDE TO ARTIFICIAL INTELLIGENCE IN HEALTHCARE. Copyright: The Medical Futurist 2019. CONTENTS. Part I. THE BASICS OF ARTIFICIAL INTELLIGENCE. Artificial intelligence: a reference point for innovation. Fears and expectations about A.I. Let the quest for balanced views on A.I. begin. What is Artificial Intelligence? Narrow, general, or super?
Principles for Artificial Intelligence (AI) and its application in ...
Implementing AI in healthcare requires careful consideration beyond merely introducing new technologies. Key issues include ensuring AI tools are rigorously tested for safety and eficacy, avoiding reliance solely on lab-based evaluations.
Artificial Intelligence
What is AI? A primer for clinicians. Patient safety. The doctor and patient relationship. Public acceptance and trust. Accountability for decisions. Bias, inequality and unfairness. Data quality, consent and information governance. Training and education. Medical research. The …
Artificial Intelligence In Health Care - Massachusetts Institute of ...
Understand the potential for AI to transform health care, from disease diagnosis to hospital optimization and patient care. Learn how adopting an integrated approach to AI can improve hospital management and optimization.
Artificial intelligence in healthcare: An essential guide for health ...
This article provides a guide to understand the fundamentals of AI technologies (ie, machine learning, natural language processing, and AI voice assistants) as well as their proper use in healthcare. It also provides practical recom-mendations to help decision-makers develop an AI strategy that can support their digital healthcare transformation.
Artificial Intelligence in Healthcare - AMA
Artificial Intelligence in Healthcare. This document outlines the AMA’s position on the application of artificial intelligence (AI) and AI tools in healthcare. AI tools for the purpose of this position statement include automated decision making (ADM) and application of Large Language Models (LLMs) in healthcare.
AI Doctor: The Rise of Artificial Intelligence in Healthcare: A Guide ...
Section II (“Applications of AI in Healthcare”) examines in great detail the opportunities for AI health care applications in diagnostics, therapeu-tics, decision support, population health, clinical workflows, administration, and the related basic life sciences.
Transforming healthcare with AI - McKinsey & Company
Artificial intelligence (AI) has the potential to transform how care is delivered. It can support improvements in care outcomes, patient experience and access to healthcare services. It can increase productivity and the efficiency of care delivery and allow healthcare systems to provide more and better care to more people.
Using Artificial Intelligence to Support Healthcare Decisions
In healthcare, AI has made advances in analysing data about how diseases progress. It is also being used to identify molecules that could make new drugs, diagnose medical conditions more precisely, predict how patients will respond to treatment, and improve the planning of resources such as hospital beds.
Artificial Intelligence in Healthcare - Executives for Health …
Intelligence affords the ability to spot trends and patterns across certain groups, monitor overall plan performance across populations and take advantage of a variety of data types, such as social determinants of health, environmental, genomic and behavioral health.
BLUEPRINT FOR TRUSTWORTHY AI IMPLEMENTATION GUIDANCE …
The use of artificial intelligence (AI) in healthcare offers enormous potential for accelerating clinical research and improving the quality and efficiency of healthcare delivery. However, a growing body of evidence demonstrates that the adoption of AI and the subset of AI known as
Artificial Intelligence: How to get it right
Artificial Intelligence (AI) has the potential to make a significant difference to health and care. A broad range of techniques can be used to create Artificially Intelligent Systems (AIS) to carry out or augment health and care tasks that have until now been completed by humans, or have not been possible previously; these
2024 Artificial Intelligence for Health - World Health Organization
We develop standards, policies, and guidance to guide countries on safe and ethical use of AI in health. We enable countries to establish safe and equitable AI ecosystems in health. We facilitate knowledge sharing by delivering workshops and briefings to support implementation of …
Artificial Intelligence and Primary Care - Royal College of General ...
This paper discusses and provides a brief overview of the role and potential impact that artificial intelligence (AI) may have in primary care. It outlines the basic principles of AI, summarising where it may influence primary care and explores the possible role of …
Ethics and governance of artificial intelligence for health
The original WHO guidance on ethics and governance of AI for health (1) examined various approaches to machine learning and various applications of AI in health care but did not specifically examine generative AI or LMMs.
AI Doctor: The Rise of Artificial Intelligence in Healthcare: A Guide ...
see artificial intelligence AI adoption in healthcare, drivers of availability of data, 92–95 inefficient care pathways, 106–109 key drivers of, 92 macroeconomic drivers, 91 non‐personalized care, 106–109 policy and regulatory, 95–105 shortage of healthcare resources, 105, 105–106, 106 technical issues, 91 AI chatbots, 190 “AI ...
Artificial Intelligence in Healthcare: 10 Questions
By applying advanced analytics and artificial intelligence (AI) to data, healthcare providers can identify insights and patterns that enhance clinical, operational, and financial decision-making. Here’s a closer look at AI and the latest research on how, when, and where it will impact healthcare delivery. 1.
Artificial Intelligence (AI) Strategy - HHS.gov
HHS’ regulatory responsibility spans all aspects of healthcare including standards for healthcare delivery, payments, medical device software, medical products and food, and privacy to ensure compliance, safety, and effectiveness. AI can be leveraged to reduce regulatory burdens and
Understanding healthcare workers confidence in AI
influencing healthcare workers’ confidence in artificial intelligence (AI) technologies and how these can inform the development of related education and training. The research follows the Topol Review (2019) recommendation to develop a healthcare workforce able and willing to use AI and robotics, and is part of Health
A National Policy Roadmap for Artificial Intelligence in Healthcare
A fully funded national plan by 2025 designed to create an AI-enabled Australian healthcare system capable of delivering personalised healthcare, safely, ethically and sustainably supported by a vibrant AI industry sector that creates jobs and exports to the world, alongside an AI-aware workforce and AI-savvy consumers.
