Artificial Intelligence

Artificial intelligence (AI) is transforming healthcare with a promise to improve clinical decision-making, patient outcomes and hospital systems’ efficiency. Yet, challenges related to patient safety and model bias, validation and integration into existing systems remain. This brief offers specific actions that will contribute to a national strategy to develop an AI ecosystem suited to Australia’s health system.

Explains how artificial intelligence is applied to medicine, illustrating not only its enormous potential but also ancillary issues and the limits and risks inherent in its use on a large scale. The book focuses on the intersection between medicine and AI and its implications on the impact of human health care delivery. Topics discussed include wearable devices, health data, Internet of Things, virtual reality, robotic assistance system, and digital intelligence in the health sector. Additionally, sections discuss diagnostics and decision-making systems and machine/deep learning in clinical setting.

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.

Undeniable, inescapable, exhilarating and breaking free from the exclusive domain of science, artificial intelligence has become our main preoccupation. A major generator of new mathematical thinking, AI is the result of easy access to information and data, as facilitated by computer technology. Big Data has come to be seen as an unlimited source of knowledge, the use of which is still being fully explored, but its industrialization has swiftly followed in the footsteps of mathematicians; today's tools are increasingly designed to replace human beings, which comes with social and philosophical consequences. Drawing on examples of scientific work and the insights of experts, this book offers food for thought on the consequences and future of AI technology in education, health, the workplace and aging.

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.

This book presents a collection of papers revealing the impact of advanced computation and instrumentation on healthcare. It highlights the increasing global trend driving innovation for a new era of multifunctional technologies for personalized digital healthcare. Moreover, it highlights that contemporary research on healthcare is performed on a multidisciplinary basis comprising computational engineering, biomedicine, biomedical engineering, electronic engineering, and automation engineering, among other areas.

The report identifies the ethical challenges and risks with the use of artificial intelligence of health, six consensus principles to ensure AI works to the public benefit of all countries. It also contains a set of recommendations that can ensure the governance of artificial intelligence for health maximizes the promise of the technology and holds all stakeholders – in the public and private sector – accountable and responsive to the healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use.

The guidance outlines over 40 recommendations for consideration by governments, technology companies, and health care providers to ensure the appropriate use of LMMs to promote and protect the health of populations. LMMs can accept one or more type of data inputs, such as text, videos, and images, and generate diverse outputs not limited to the type of data inputted. LMMs are unique in their mimicry of human communication and ability to carry out tasks they were not explicitly programmed to perform. LMMs have been adopted faster than any consumer application in history, with several platforms – such as ChatGPT, Bard and Bert – entering the public consciousness in 2023.

Modern medicine and healthcare are highly dependent on engineering, employing instrumentation and computer systems to aid investigation, diagnosis, treatment and patient management. The significant developments in the field of computational intelligence, combined with the emergence of high-performance computing is impacting society in many ways, and the health sector is no exception. The interface of high-performance computing, computational intelligence and medical science, has seen the emergence of intelligent medical systems. These systems can provide a deeper insight into many healthcare and medical problems.

Provides a comprehensive survey of artificial intelligence concepts and methodologies with real-life applications in cardiovascular medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and data science domains. The book's content consists of basic concepts of artificial intelligence and human cognition applications in cardiology and cardiac surgery. This portfolio ranges from big data, machine and deep learning, cognitive computing and natural language processing in cardiac disease states such as heart failure, hypertension and pediatric heart care.

In the era of 'Algorithmic Medicine', the integration of Artificial Intelligence (AI) in healthcare holds immense potential to address critical challenges faced by the industry. Drawing upon the expertise and experience of the authors in medicine, data science, medical informatics, administration, and entrepreneurship, this textbook goes beyond theoretical discussions to outline practical steps for transitioning AI from the experimental phase to real-time clinical integration.