This page contains books on artificial intelligence.
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AI in Health: Huge Potential, Huge Risks by 
Artificial Intelligence-Based Healthcare Systems by
This book explores new applications in the field of science and technology for healthcare systems. The main focus of this book is to devise smart, efficient and robust solutions for the health care sector to serve the major population of rural areas. Artificial Intelligence-based Healthcare Systems encourages scientists, engineers, and scholars across the multiple disciplines to design smart intelligent innovations on rural healthcare issues and motivate to collaborate multiple ideas to design best solutions. It also helps the readers at various levels of knowledge to further enhance their understanding for new tools and smart solutions.
Artificial Intelligence for Health in New Zealand Hauora i te Atamai Iahiko by
The health sector in New Zealand is facing challenges. These include increasing demand, rising consumer expectations, and the pressures of an ageing population. These factors are straining the health workforce, increasing costs and limiting access to care.
The AI Forum’s latest research report describes how AI promises to bring significant clinical, workforce and cost benefits to the health sector, as well as personalise medical care. It can help with predicting disease and injury; and mine vast quantities of literature for research insights.
It can also assist in novel drug discovery; augment the work of human specialists through image analysis and robotic surgery; automate hospital processes; free up more time for doctor-patient interaction and provide personalised treatment.
Artificial Intelligence in Covid-19 by
This book deals with the advantages of using artificial intelligence (AI) in the fight against the COVID-19 and against future pandemics that could threat humanity and our environment. This book is a practical, scientific and clinically relevant example of how medicine and mathematics will fuse in the 2020s, out of external pandemic pressure and out of scientific evolutionary necessity. This book contains a unique blend of the world's leading researchers, both in medicine, mathematics, computer science, clinical and preclinical medicine, and presents the research front of the usage of AI against pandemics. Equipped with this book the reader will learn about the latest AI advances against COVID-19, and how mathematics and algorithms can aid in preventing its spreading course, treatments, diagnostics, vaccines, clinical management and future evolution.
Artificial Intelligence in Health Professions Education: Proceedings of a Workshop by
The National Academies Global Forum on Innovation in Health Professional Education hosted a multi-day workshop series in March and April 2023 to explore the potential of artificial intelligence (AI) in health professions education. Speakers at the workshops provided background on AI; discussed the social, cultural, policy, legal, and regulatory considerations to integrating AI into health care and training; considered the skills health professionals will need as educators and providers to effectively use AI in practice; and explored needs for educating the next generation of health workers. Speakers took consideration of the bias, burden, health equity concerns that introducing AI into clinical education would bring. This Proceedings of a Workshop summarizes the discussions held during the workshop.
Capturing the Benefits of AI in Healthcare for Aotearoa New Zealand: A Rapid Report from the Prime Minister’s Chief Science Advisor Kaitohutohu Mātanga Pūtaiao Matua ki te Pirimia by
In the wake of COVID-19, pressures on our health services and our healthcare professionals are immense and well-publicised. Though far from a panacea, artificial intelligence (AI) offers opportunities to strengthen the health system, support healthcare professionals, and improve the health of all New Zealanders. Taking a broad perspective of AI and its promise to improve health outcomes, we imagine an Aotearoa New Zealand in which the benefits of AI have been captured, at least in primary care, in our Vision. The horizon of this rapid report is the near future, providing an overview of feasible opportunities within this time frame. We explored the possible benefits that AI may provide within the next five years and ways we can plan, manage, and deliver better outcomes in administrative areas, care delivery and health equity, population health and policy, and research. The report also features case studies from New Zealand and around the world to give a sense of the opportunities. Additionally, through the engagement process we were able to hear about the journey for some of our local case studies, and some of the hurdles they had to navigate are captured throughout the report. In common with other countries, there are, of course, some challenging issues with which to engage as we adopt AI, including ethics, consent, governance, equity, and the risks of a digital divide. Our discussions also highlighted some themes unique to New Zealand such as digital infrastructure, nurturing data as taonga, population dynamics, and enabling access. We provide recommendations to help build an ecosystem that can fully capture the benefits of AI in the New Zealand health sector.
Collective Action for Responsible AI in Health by
Artificial intelligence will have profound impacts across health systems, transforming health care, public health, and research. Responsible AI can accelerate efforts toward health systems being more resilient, sustainable, equitable, and person-centred. This paper provides an overview of the background and current state of artificial intelligence in health, perspectives on opportunities, risks, and barriers to success. The paper proposes several areas to be explored for policy makers to advance the future of responsible AI in health that is adaptable to change, respects individuals, champions equity, and achieves better health outcomes for all. The areas to be explored relate to trust, capacity building, evaluation, and collaboration. This recognises that the primary forces that are needed to unlock the value from artificial intelligence are people-based and not technical. The OECD is ready to support efforts for co-operative learning and collective action to advance the use of responsible AI in health.
Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-modal Models by
Artificial Intelligence (AI) refers to the capability of algorithms integrated into systems and tools to learn from data so that they can perform automated tasks without explicit programming of every step by a human. Generative AI is a category of AI techniques in which algorithms are trained on data sets that can be used to generate new content, such as text, images or video. This guidance addresses one type of generative AI, large multi-modal models (LMMs), which can accept one or more type of data input and generate diverse outputs that are not limited to the type of data fed into the algorithm. It has been predicted that LMMs will have wide use and application in health care, scientific research, public health and drug development. LMMs are also known as “general-purpose foundation models”, although it is not yet proven whether LMMs can accomplish a wide range of tasks and purposes.
Ethics of Artificial Intelligence: Case Studies and Options for Addressing Ethical Challenges by
This open access collection of AI ethics case studies is the first book to present real-life case studies combined with commentaries and strategies for overcoming ethical challenges. Case studies are one of the best ways to learn about ethical dilemmas and to achieve insights into various complexities and stakeholder perspectives. Given the omnipresence of AI ethics in academic, policy and media debates, the book will be suitable for a wide range of audiences, from scholars of different disciplines (e.g. AI science, ethics, politics, philosophy, economics) to policy-makers, lobbying NGOs, teachers and the educated public.
Introduction to Artificial Intelligence by
This book aims to provide physicians and scientists with the basics of Artificial Intelligence (AI) with a special focus on medical imaging. The contents of the book provide an introduction to the main topics of artificial intelligence currently applied on medical image analysis. The book starts with a chapter explaining the basic terms used in artificial intelligence for novice readers and embarks on a series of chapters each one of which provides the basics on one AI-related topic. The second chapter presents the programming languages and available automated tools that enable the development of AI applications for medical imaging. The third chapter endeavours to analyse the main traditional machine learning techniques, explaining algorithms such as random forests, support vector machines as well as basic neural networks. The applications of those machines on the analysis of radiomics data is expanded in the fourth chapter to allow the understanding of algorithms used to build classifiers for the diagnosis of disease processes with the use of radiomics. Chapter five provides the basics of natural language processing which has revolutionized the analysis of complex radiological reports and chapter six affords a succinct introduction to convolutional neural networks which have revolutionized medical image analysis enabling automated image-based diagnosis, image enhancement (e.g. denoising), protocolling etc. The penultimate chapter provides an introduction to data preprocessing for use in the aforementioned artificial intelligence applications. The book concludes with a chapter demonstrating AI-based tools already in radiological practice while providing an insight about the foreseeable future. It will be a valuable resource for radiologists, computer scientists and postgraduate students working on medical image analysis.
Multiple Perspectives on Artificial Intelligence in Healthcare: Opportunities and Challenges by
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 National Policy Roadmap for Artificial Intelligence in Healthcare by
This National Policy Roadmap for AI in Healthcare (The Roadmap) delivers a policy agenda for AI in Healthcare. It delivers a critically needed co-ordinated national approach, with the aim of bringing Australia into line with comparable nations who have already made substantial progress in investment and adoption of AI.
Priorities for an AI Health Care Strategy by 
Regulatory Considerations on Artificial Intelligence for Health by
This publication is a general, high-level and nonexclusive overview of key regulatory considerations in topic areas developed by the WG-RC to support the overarching FG-AI4H framework. Recognizing that a single publication cannot address the specifics of the various AI systems that can be used for therapeutic development or health-care applications in general, the WG-RC’s overview will highlight some of the key regulatory principles and concepts – such as risk–benefit assessments and considerations for the evaluation and monitoring of the performance of AI systems developed using AI technologies. Throughout the process of developing this publication, the WG-RC took into consideration different stakeholder perspectives, as well as different global and regional settings. The WG-RC’s overview is not intended as guidance, as a regulatory framework or policy. Rather, it is meant as a listing of key regulatory considerations and a resource for all relevant stakeholders – including developers who are exploring and using AI technologies and developing AI systems, regulators who might be in the process of identifying approaches to manage and facilitate AI systems, manufacturers who design and develop AI systems that are embedded in medical devices, and health practitioners who deploy and use such medical devices and AI systems.
Safe and responsible AI in Australia Consultation: Australian Government’s Interim Response by
This discussion paper focuses on governance mechanisms to ensure AI is developed and used safely and responsibly in Australia. These mechanisms can include regulations, standards, tools, frameworks, principles and business practices. The paper builds on the recent Rapid Research Report on Generative AI delivered by the government’s National Science and Technology Council.
Trustworthy AI in Aotearoa AI Principles by
Artificial intelligence (AI) can drive significant economic and social benefits for New Zealand. But it also introduces a range of risks and challenges to New Zealand society that cannot be overlooked. To help maintain public trust in the development and use of AI in New Zealand, the Law, Society and Ethics Working Group of the AI Forum has published a set of guiding principles for “Trustworthy AI in Aotearoa New Zealand” (the AI Principles). Those principles are designed to provide high-level guidance for anyone involved in designing, developing and using artificial intelligence in New Zealand (AI stakeholders), with the goal of ensuring New Zealanders have access to trustworthy AI.
Artificial Intelligence in Anesthesiology by
Considering the rapid developments in digital and information technologies, artificial intelligence has long been a hot topic in medicine. This book discusses applications of artificial intelligence in anaesthesiology, including control of anesthesia, risk prediction, ultrasound guidance, pain management, and operating room logistics. This book first defines basic concepts of AI, and give a brief overview of a few algorithms frequently used in AI and machine learning. A review of current AI and machine learning applications for the prediction of anesthesia conditions is also discussed, including those for the prediction of difficult airways before surgery, of adverse events and sedation effects during surgery, and of vomiting and pain after surgery. Even without extensive promotion and clinical application, AI is in development in anesthesiology; furthermore, it has a great deal of potential to maintain further development in the future. Lastly, ethical and safety considerations are discussed alongside AI limitations and challenges in anesthesiology.
Artificial Intelligence and Ophthalmology by
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.
Artificial Intelligence in Cardiothoracic Imaging by
This book provides an overview of current and potential applications of artificial intelligence (AI) for cardiothoracic imaging. Most AI systems used in medical imaging are data-driven and based on supervised machine learning. Clinicians and AI specialists can contribute to the development of an AI system in different ways, focusing on their respective strengths. Unfortunately, communication between these two sides is far from fluent and, from time to time, they speak completely different languages. Mutual understanding and collaboration are imperative because the medical system is based on physicians' ability to take well-informed decisions and convey their reasoning to colleagues and patients. This book offers unique insights and informative chapters on the use of AI for cardiothoracic imaging from both the technical and clinical perspective. It is also a single comprehensive source that provides a complete overview of the entire process of the development and use of AI in clinical practice for cardiothoracic imaging. The book contains chapters focused on cardiac and thoracic applications as well more general topics on the potentials and pitfalls of AI in medical imaging. Separate chapters will discuss the valorization, regulations surrounding AI, cost-effectiveness, and future perspective for different countries and continents. This book is an ideal guide for clinicians (radiologists, cardiologists etc.) interested in working with AI, whether in a research setting developing new AI applications or in a clinical setting using AI algorithms in clinical practice. The book also provides clinical insights and overviews for AI specialists who want to develop clinically relevant AI applications.
Artificial Intelligence in Dentistry by
This comprehensive book focuses on various aspects of artificial intelligence in dentistry, assisting dentists, specialists, and scientists in advancing their understanding, knowledge, training, and expertise in this field of artificial intelligence. Readers will learn about AI-supported pathways for the diagnosis and treatment of dental caries, periodontal bone loss, impacted teeth, periapical lesions, crown, and root fractures, working length determination, and detecting root and canal morphology, TMJ disorders, detection of obstructive sleep apnea, oral mucosal lesions, and many more. Prediction tasks include the estimation of retreatment needs and third molar eruption. Critical information on applications of AI in the field of Oral and Maxillofacial Radiology, Implants, Endodontics, Prosthodontics, Restorative dentistry, Oral surgery, Periodontics, and Orthodontics. Gain valuable insight into studies applying machine learning based on Machine Learning (ML), Deep Learning (DL), and Artificial Neural Networks (ANN). Explore the technical aspects and medical applications of AI in dentistry. Additionally, discover cutting-edge topics like 3D and bioprinting applications of AI and its integration into dental education. All the chapters provide thorough, evidence-based data on AI and its implications in oral health, bridging the gap between knowledge and practical application. The book explains the advantages, disadvantages, and limitations of AI in dental health. Delve into the medico-legal aspects of AI to navigate this cutting-edge landscape responsibly. Learn about applications of Machine Learning and Artificial Intelligence in the Covid-19 Pandemic. Extensive information on deep learning in image processing, including various types of neural networks, image segmentation, enhancement, reconstruction, and registration. This book concludes with an exploration of AI's exciting potential and future perspectives in the dental field, paving the way for a new era of oral healthcare. Don't miss out on this unique resource for AI in Dentistry, which empowers you to stay at the forefront of innovation and embrace the AI revolution in Dentistry. Be prepared for the future of dentistry.
Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks by
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.
Generative AI in Higher Education: The ChatGPT Effect by
Chan and Colloton's book is one of the first to provide a comprehensive examination of the use and impact of ChatGPT and Generative AI (GenAI) in higher education.
Task Force Report on Artificial Intelligence and Emerging Digital Technologies by 
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