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.

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 constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

The edition will cover proceedings of the second International conference on digital health Technologies (ICDHT 2019). The conference will address the topic of P4 medicine from the information technology point of view, and will be focused on the following topics: - Artificial Intelligence for health • Knowledge extraction • Decision-aid systems • Data analysis and risk prediction • Machine learning, deep learning - Health data processing • Data preprocessing, cleaning, management and mining • Computer-aided detection • Big data analysis, prediction and prevention • Cognitive algorithms for healthcare handling dynamic context management • Augmented reality, Motion detection and activity recognition - Devices, infrastructure and communication • Wearable & connected devices • Communication infrastructures, architectures and standards Blockchain for e-Health • Computing/storage infrastructures for e-Health • IoT devices & architectures for Smart Healthcare - Health information systems • Telemedicine, Teleservices • Computing/storage infrastructures for e-Health • Clinical Data Visualisation Standards - Security and privacy for e-health • Health data Analytics for Security and Privacy • E-health Software and Hardware Security • Embedded Security for e-health - Applications in P4 medicine

Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine

Neurotechnologies such as brain-computer interfaces (BCIs), which allow technical devices to be used with the power of thought or concentration alone, are no longer a futuristic dream or, depending on the viewpoint, a nightmare. Moreover, the combination of neurotechnologies and AI raises a host of pressing problems. Now that these technologies are about to leave the laboratory and enter the real world, these problems and implications can and should be scrutinized. This volume brings together scholars from a wide range of academic disciplines such as philosophy, law, the social sciences and neurosciences, and is unique in terms of both its focus and its methods. The latter vary considerably, and range from philosophical analysis and phenomenologically inspired descriptions to legal analysis and socio-empirical research. This diversified approach allows the book to explore the entire spectrum of philosophical, normative, legal and empirical dimensions of intelligent neurotechnologies. Philosophical and legal analyses of normative problems are complemented by a thorough empirical assessment of how BCIs and other forms of neurotechnology are being implemented, and what their measurable implications are. To take a closer look at specific neurotechnologies, a number of applications are addressed. Case studies, previously unidentified issues, and normative insights on these cases complement the rich portrait this volume provides. Clinicians, philosophers, lawyers, social scientists and engineers will greatly benefit from the collection of articles compiled in this book, which will likely become a standard reference work on the philosophy of intelligent neurotechnologies.

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