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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 discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses.
New developments in machine learning (ML) and artificial intelligence (AI) hold great promise to revolutionize mental health care. In this context, ML and AI have been deployed for several different goals, including 1) the early detection of mental disorders, 2) the optimization of personalized treatments based on the individual characteristics of patients, 3) the better characterization of disorders detrimental to mental well-being and quality of life, as well as a better description of projected trajectories over time, and 4) the development of new treatments for mental health care. Despite their great potential to transform mental health care and occasional breakthroughs, ML and AI have not yet fully achieved these goals. This research topic aims to bridge the gap between the potential uses of ML and AI and their practical application in standard mental health care. More specifically, we welcome original research submissions applying ML and AI to promote public health by reducing the burden of chronic disorders with detrimental effects on well-being (e.g., psychopathological distress), and improving quality of life. We also welcome submissions applying ML and AI in heterogeneous datasets (e.g., subjective scales and questionnaires, biomarkers, (neuro)psychological assessments, etc.) from Big Data sources (e.g., large datasets of clinical populations, electronic health records from nationally representative cohorts, and/or biobanks, studies using experiencing sampling methods, etc.) to gain mechanistic insight on how different chronic conditions associated with psychopathological distress can affect patient well-being and quality of life. Finally, we also welcome opinion papers and reviews on how to develop AI applications in mental health care responsibly, while integrating biopsychosocial aspects of patients to promote better mental health care.
Artificial intelligence (AI) and machine learning offer immense potential to transform psychiatry and mental healthcare. As these technologies continue to evolve rapidly, ensuring responsible and ethical implementation remains crucial. This definitive ebook provides psychiatrists, developers, policymakers and other stakeholders a comprehensive guide to leveraging AI in psychiatry in a thoughtful, prudent manner. From Improving Diagnosis and Treatment to Enabling Personalized Care, AI Promises to Enhance Patient Outcomes Exciting opportunities lie ahead to utilize AI and machine learning to improve psychiatric diagnosis, enhance treatment methodologies, and enable more personalized mental healthcare. AI-enabled solutions like predictive analytics, digital phenotyping, and conversational agents can provide benefits ranging from earlier intervention to reduced stigma. However, the limitations and clinical validity of these innovations must also be weighed carefully. Practical Guidance Offered on Mitigating Algorithmic Bias, Ensuring Privacy, and Obtaining Consent with AI The responsible design, testing, and deployment of AI tools is emphasized throughout this ebook. Practical guidance is offered on crucial considerations like mitigating algorithmic bias, ensuring patient privacy, and obtaining informed consent when AI is used in assessment or treatment. Establishing trust between patients, psychiatrists, and intelligent systems emerges as an important prerequisite for the effective integration of AI in mental healthcare. The Thoughtful Integration of AI with Psychiatry Poised to Increase Access to Quality Mental Healthcare Grounding discussions in real-world examples, this ebook advocates for the judicious adoption of AI in psychiatry. The thoughtful integration of these technologies stands ready to increase access to quality mental health services, reduce stigma, and enable more positive outcomes for diverse populations.
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.
Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.
Healthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field. This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.
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
Artificial intelligence (AI) has rapidly evolved in recent years, making a significant impact on the field of psychology. With the development of AI-powered diagnostic tools for mental health professionals, therapists and counselors now have access to more accurate and efficient methods of assessing and treating psychological disorders. These tools use advanced algorithms to analyze patient data and provide personalized treatment plans, leading to improved outcomes and treatment efficacy. One of the most exciting developments in AI for psychology professionals is the use of AI chatbots for therapy and counseling services. These virtual assistants are able to engage with patients in real-time, providing support and guidance whenever it is needed. AI chatbots can also track patient progress and provide valuable insights to therapists, helping them to tailor their treatment plans accordingly. In addition to AI chatbots, AI-driven cognitive behavioral therapy programs have also revolutionized the way psychological disorders are treated. These programs use advanced algorithms to analyze patient behavior and provide personalized interventions, leading to more effective treatment outcomes. AI-based emotional intelligence assessments have also been developed, allowing professionals to gain deeper insights into patients' emotional states and tailor their treatment plans accordingly. AI algorithms have also been instrumental in predicting patient behavior and detecting psychological disorders at an early stage. By analyzing large amounts of patient data, AI tools are able to identify patterns and trends that may indicate the presence of a psychological disorder. This early detection allows for prompt intervention and treatment, leading to improved patient outcomes. AI applications have also been used to enhance virtual reality therapy experiences, providing patients with immersive and personalized treatment options. AI-powered mental health monitoring and intervention systems have also been developed, allowing professionals to track patient progress in real-time and intervene when necessary. Overall, the evolution of artificial intelligence in psychology has revolutionized the field, providing professionals with powerful tools to improve patient outcomes and treatment efficacy.