Can Artificial Intelligence And Big Data Analytics Save The Future Of Psychiatry

This book is the second of the series about the imperatives for the search for new psychiatry. As stated in my recent 2021 book about: The Search for New Psychiatry, current psychiatric practices have failed many: patients and their families, their doctors and the society at large. That was the end of the 2021 book and the beginning of this book as a follow up in search for pathways to a new and more effective science-based practice Based on its major contributions to the recent successful and expedient development of the Covid 19 vaccines, I am proposing the same pathway of using the new revolution in informatics as the way to save and secure the future of psychiatry and that is what I am recommending in this book reaping the benefit of AI and Big Data Analytics but with a wide open eye on its limits, reliability, risks, unforeseen or unintentional harms. Part Two of the book deals with a number of perineal and also new challenges that continue to require better understanding and resolution. Among the phenomenological and nosological challenges, the recent development by Neurology of its subspeciality of Behavioral Neurology in competition to Neuropsychiatry, is reviewed in terms of an opportunity for integration of the tow subspecialities towards the creation of a new third field of "Clinical Neurosciences". Other challenges included are: The Subjective /Objective Dichotomy, Lunacy and the Moon- reflections on the interactions of the brain and environment and Woke Psychiatry, what is it? Several other clinical challenges include: The Past is Coming Back as The Future -The Rise, Fall and Rise Again of Psychedelics, Loneliness as the silent disorder and several other challenges. At the end, a postscript has been hastily added in memory of a close friend, a pioneering psychopharmacologist but above all an empathic humanist, Professor Thomas Arthur Ban or as he always preferred, Tom.

This book is the second of the series about the imperatives for the search for new psychiatry. As stated in my recent 2021 book about: The Search for New Psychiatry, current psychiatric practices have failed many: patients and their families, their doctors and the society at large. That was the end of the 2021 book and the beginning of this book as a follow up in search for pathways to a new and more effective science-based practice Based on its major contributions to the recent successful and expedient development of the Covid 19 vaccines, I am proposing the same pathway of using the new revolution in informatics as the way to save and secure the future of psychiatry and that is what I am recommending in this book reaping the benefit of AI and Big Data Analytics but with a wide open eye on its limits, reliability, risks, unforeseen or unintentional harms. Part Two of the book deals with a number of perineal and also new challenges that continue to require better understanding and resolution. Among the phenomenological and nosological challenges, the recent development by Neurology of its subspeciality of Behavioral Neurology in competition to Neuropsychiatry, is reviewed in terms of an opportunity for integration of the tow subspecialities towards the creation of a new third field of “Clinical Neurosciences”. Other challenges included are: The Subjective /Objective Dichotomy, Lunacy and the Moon- reflections on the interactions of the brain and environment and Woke Psychiatry, what is it? Several other clinical challenges include: The Past is Coming Back as The Future -The Rise, Fall and Rise Again of Psychedelics, Loneliness as the silent disorder and several other challenges. At the end, a postscript has been hastily added in memory of a close friend, a pioneering psychopharmacologist but above all an empathic humanist, Professor Thomas Arthur Ban or as he always preferred, Tom.

This book integrates the concepts of big data analytics into mental health practice and research. Mental disorders represent a public health challenge of staggering proportions. According to the most recent Global Burden of Disease study, psychiatric disorders constitute the leading cause of years lost to disability. The high morbidity and mortality related to these conditions are proportional to the potential for overall health gains if mental disorders can be more effectively diagnosed and treated. In order to fill these gaps, analysis in science, industry, and government seeks to use big data for a variety of problems, including clinical outcomes and diagnosis in psychiatry. Multiple mental healthcare providers and research laboratories are increasingly using large data sets to fulfill their mission. Briefly, big data is characterized by high volume, high velocity, variety and veracity of information, and to be useful it must be analyzed, interpreted, and acted upon. As such, focus has to shift to new analytical tools from the field of machine learning that will be critical for anyone practicing medicine, psychiatry and behavioral sciences in the 21st century. Big data analytics is gaining traction in psychiatric research, being used to provide predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Personalized Psychiatry – Big Data Analytics in Mental Health provides a unique opportunity to showcase innovative solutions tackling complex problems in mental health using big data and machine learning. It represents an interesting platform to work with key opinion leaders to document current achievements, introduce new concepts as well as project the future role of big data and machine learning in mental health.

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 volume provides an interdisciplinary collection of essays from leaders in various fields addressing the current and future challenges arising from the implementation of AI in brain and mental health. Artificial Intelligence (AI) has the potential to transform health care and improve biomedical research. While the potential of AI in brain and mental health is tremendous, its ethical, regulatory and social impacts have not been assessed in a comprehensive and systemic way. The volume is structured according to three main sections, each of them focusing on different types of AI technologies. Part 1, Big Data and Automated Learning: Scientific and Ethical Considerations, specifically addresses issues arising from the use of AI software, especially machine learning, in the clinical context or for therapeutic applications. Part 2, AI for Digital Mental Health and Assistive Robotics: Philosophical and Regulatory Challenges, examines philosophical, ethical and regulatory issues arising from the use of an array of technologies beyond the clinical context. In the final section of the volume, Part 3 entitled AI in Neuroscience and Neurotechnology: Ethical, Social and Policy Issues, contributions examine some of the implications of AI in neuroscience and neurotechnology and the regulatory gaps or ambiguities that could potentially hamper the responsible development and implementation of AI solutions in brain and mental health. In light of its comprehensiveness and multi-disciplinary character, this book marks an important milestone in the public understanding of the ethics of AI in brain and mental health and provides a useful resource for any future investigation in this crucial and rapidly evolving area of AI application. The book is of interest to a wide audience in neuroethics, robotics, computer science, neuroscience, psychiatry and mental health.

Medical internet of things (IoT)-based applications are being utilized in several industries and have been shown to provide significant advantages to users in critical health applications. Artificial intelligence (AI) plays a key role in the growth and success of medical IoT applications and IoT devices in the medical sector. To enhance revenue, improve competitive advantage, and increase consumer engagement, the use of AI with medical IoT should be encouraged in the healthcare and medical arena. Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications provides greater knowledge of how AI affects healthcare and medical efficacy in order to improve outputs. It focuses on a thorough and comprehensive introduction to machine learning. Covering topics such as patient treatment, cyber-physical systems, and telemedicine, this premier reference source is a dynamic resource for hospital administrators, medical professionals, government officials, students and faculty of higher education, librarians, researchers, and academicians.

Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. Summarizes AI advances for use in mental health practice Includes advances in AI based decision-making and consultation Describes AI applications for assessment and treatment Details AI advances in robots for clinical settings Provides empirical data on clinical efficacy Explores practical issues of use in clinical settings

Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. Presents theories, methods and approaches in which data analytic techniques are used for medical data Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases Discusses social, behavioral, and medical fake news analytics for medical information systems