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.

This WHO Guidance document discusses ethical and governance issues as they arise in the use of artificial intelligence (AI) for health. It contains a set of principles, recommendations, and checklists for selected end-users. The target audience is Ministries of Health, AI developers, health care workers, and industry.

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

This is an open access title available under the terms of a CC BY-NC-ND 4.0 License. It is free to read, download and share on Elgaronline, thanks to generous funding support from Hamad Bin Khalifa University (HBKU). The Research Handbook on Health, AI and the Law explores the use of AI in healthcare, identifying the important laws and ethical issues that arise from its use. Adopting an international approach, it analyses the varying responses of multiple jurisdictions to the use of AI and examines the influence of major religious and secular ethical traditions.

Artificial Intelligence (AI) technology has led to the creation of many opportunities in the field of healthcare. Like other industries, stakeholders in the healthcare sector stand to benefit tremendously from its adoption. The multifaceted benefits associated with AI are something that makes the adoption of technology constructive for the sector. That said, it is equally important to take care of the ethical, security, and safety challenges related to AI applications. AI Healthcare Applications and Security, Ethical, and Legal Considerations discusses in detail the various facets of AI integration in the healthcare sector. This book offers comprehensive information on how to integrate AI into the healthcare sector safely and ethically. Covering topics such as cybersecurity, machine learning models, and public policy, this book is an excellent resource for healthcare professionals and administrators, researchers, ethicists, legal scholars, healthcare policy makers and regulators, medical informatics and IT professionals, educators, bioethics professionals, academicians, and more.

Volume II This collection brings together work on the relevance of Wittgenstein’s philosophy to the field of Artificial Intelligence (AI). Over two volumes, our contributors cover a wide range of topics from different disciplinary approaches. In this Volume (II), contributions are centred on two major themes in the philosophy of AI: questions of value and governance. Contributions include chapters on both ethics and aesthetics and AI, as well as questions of the governance of AI systems, including legal and policy issues.