WHO consultation towards the development of guidance on ethics and governance of artificial intelligence for health


Book Description

The present document is the report of the first expert meeting, which was held 2–4 October 2019 at WHO in Geneva. Participants discussed current and potential artificial intelligence (AI) applications for global health in the context of existing frameworks and human rights standards. The participants considered the main challenges of ethics, governance and equitable access and discussed potential solutions. The expert meeting has established a foundation for the second meeting which was held on 5 and 6 March 2020 in Copenhagen, Denmark. The publication of the WHO guidance document on Ethics and governance of AI for health is envisaged for completion in 2021.




Ethics and governance of artificial intelligence for health


Book Description

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.




Ethics and governance of artificial intelligence for health: large multi-modal models. WHO guidance


Book Description

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.




Personalized Medicine Meets Artificial Intelligence


Book Description

The book provides a multidisciplinary outlook on using Artificial Intelligence (AI)-based solutions in the field of Personalized Medicine and its transitioning towards Personalized Digital Medicine. The first section integrates different perspectives on AI-based solutions and highlights their potential in biomedical research and patient care. In the second section, the authors present several real-world examples that demonstrate the successful use of AI technologies in various contexts. These include examples from digital therapeutics, in silico clinical trials, and network pharmacology. In the final section of the book, the authors explore future directions in AI-enhanced biomedical technologies and discuss emerging technologies such as blockchain, quantum computing and the “metaverse”. The book includes discussions on the ethical, regulatory, and social implications for an AI-based personalized medicine. The integration of heterogeneous disciplines brings together multiple stakeholders and decision makers involved in the personalization of care. Clinicians, students, and researchers from academia and the industry can benefit from this book, since it provides foundational knowledge to drive advances in personalized biomedical research and health care.







Artificial Intelligence in Healthcare


Book Description

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




AI and education


Book Description

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]




Global Network of WHO Collaborating Centres for Bioethics


Book Description

In 2009 WHO established its Global Network of Collaborating Centres for Bioethics. This publication provides an overview of the activities of the Network during the years 2019-2021.




Global guidance framework for the responsible use of the life sciences


Book Description

The framework aims to provide global perspectives on principles, tools and mechanisms to support Member States and relevant stakeholders to mitigate and prevent biorisks and govern dual-use research. The framework adopts the One health approach and focuses on the role that responsible life sciences research can play in preventing and mitigating risks caused by accidents, inadvertent or deliberate misuse with the intention to cause harm to humans, nonhuman animals, plants and agriculture, and the environment. The framework is primarily intended for those who have responsibilities in the governance of biorisks, such as policy makers and regulators in charge of developing national policies to harness the potential benefits of the life sciences while constraining their risks. The framework is also directed towards scientists and research institutions, educators, trainers, project management staff, funding bodies, publishers, editors, the private sector and all relevant stakeholders that are part of the research life cycle. The governance of biorisks is an issue that should engage all countries, although countries will have different contexts, needs and starting points. Mitigating these risks will require individual and collective actions among different stakeholders and disciplines. Mitigating biorisks and governing dual-use research is a shared responsibility.




Missing links in AI governance


Book Description