This Research Topic is a follow on from the Topic Editors' successful volume I. Artificial Intelligence (AI) has the ability to perform automated/case-based reasoning, constraint processing, deep learning, and deep reinforcement learning. Recent advancements in AI techniques and GPU (graphics processing unit) computing capabilities have made it possible to process large volumes of data and extract valuable insights within a short period. Digital public health data are enormous, and harnessing AI's power can lead to exciting and ground-breaking research. Due to the current COVID-19 pandemic, AI can assist in disease surveillance methods, infectious disease modeling, non-contact temperature screening, intelligent contact tracking, detecting social/economic factors on transmission, effective health communication and misinformation detection, identifying factors that affect the mental and emotional health of the public.

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

In this issue of Heart Failure Clinics, guest editors Drs. Ragavendra R. Baliga and Dipti Itchhaporia bring their considerable expertise to the topic of Digital Health, sometimes referred to as eHealth. Top experts in the field cover key topics in the field such as using AI to better predict/develop biomarkers; telehealth in heart failure; EHR in heart failure; artificial intelligence and mechanical circulatory support; and more. - Contains 11 relevant, practice-oriented topics including devices to improve symptoms and reduce morbidity and mortality in heart failure; utilizing artificial intelligence to enhance equity in minority populations; 3-D printing in heart failure; machine learning in cardiac imaging; and more. - Provides in-depth clinical reviews on digital health, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.

This book reports on advances in fundamental and applied research at the interface between nanotechnology and biomedical engineering. Gathering peer-reviewed contributions to the 6th International Conference on Nanotechnologies and Biomedical Engineering, ICNBME held on September 20-23, 2023, in Chisinau, Republic of Moldova, this second volume of the proceedings focuses on clinical engineering and instrumentation, bioinformatics, image and signal processing techniques, and new technologies for medical diagnosis, treatment and personalised medicine. With a good balance of theory and practice, the book offers a timely snapshot of multidisciplinary research at the interface between physics, chemistry, biomedicine, materials science, and engineering.

This book explores the cutting-edge ideas that are shaping the future of innovation, design, entrepreneurship, and sustainability. The proceedings of the interdisciplinary Conference on Innovation, Design, Entrepreneurship, and Sustainable Systems offer new perspectives on these topics. This book navigates the most recent research trends and practices, and gains unique insights on how to address innovation, design, and entrepreneurship on a sustainable basis. This book is an essential reference for anyone interested in staying up to date on the latest research, whether they are academics, designers, entrepreneurs, or sustainability enthusiasts. Get your copy now and take part in the debate about the future of sustainable innovation.

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.

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.