The medical domain is home to many critical challenges that stand to be overcome with the use of data-driven clinical decision support systems (CDSS), and there is a growing set of examples of automated diagnosis, prognosis, drug design, and testing. However, the current state of AI in medicine has been summarized as “high on promise and relatively low on data and proof.” If such problems can be addressed, a data-driven approach will be very important to the future of CDSSs as it simplifies the knowledge acquisition and maintenance process, a process that is time-consuming and requires considerable human effort. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems critically reflects on the challenges that data-driven CDSSs must address to become mainstream healthcare systems rather than a small set of exemplars of what might be possible. It further identifies evidence-based, successful data-driven CDSSs. Covering topics such as automated planning, diagnostic systems, and explainable artificial intelligence, this premier reference source is an excellent resource for medical professionals, healthcare administrators, IT managers, pharmacists, students and faculty of higher education, librarians, researchers, and academicians.

The field of cardiovascular research and monitoring faces a critical challenge in the early detection of cardiovascular diseases due to limitations in existing monitoring methods. These methods lack accuracy, power efficiency, and compactness, creating a gap in effective intervention and patient outcomes. This pressing problem necessitates advanced solutions that can enhance the capabilities of wearable and implantable electrocardiography (ECG) sensors for accurate and timely detection of conditions like cardiac arrhythmia and heart failure. Wearable and Implantable Electrocardiography for Early Detection of Cardiovascular Diseases presents a comprehensive solution to address these challenges. Written by esteemed scholars with experience in academia and research, this groundbreaking book introduces innovative approaches to enhance ECG sensors. It introduces a novel low-noise and low-power capacitive feedback amplifier based on a current-reused operational transconductance amplifier (OTA), incorporating digitalization techniques and threshold converters to enable the accurate extraction of vital data points. The book emphasizes reduced power consumption and circuit size to ensure energy-efficient and compact monitoring solutions. Targeting academic scholars, researchers, and professionals in the field, this essential resource covers a wide range of topics and equips readers with valuable insights and innovative solutions to overcome existing limitations. By utilizing the knowledge and tools shared in this book, scholars and professionals can drive advancements in the early detection and management of cardiovascular diseases, improving patient care and outcomes.

In the post-COVID-19 healthcare landscape, the demand for smart healthcare solutions and precision medicine systems has grown significantly. To address these challenges, the book AI and IoT-Based Technologies for Precision Medicine provides a comprehensive resource for doctors, researchers, engineers, and students. By leveraging AI and IoT technologies, the book equips healthcare professionals with advanced tools and methodologies for predictive disease analysis, informed decision-making, and other aspects of precision medicine. This resource bridges the gap between theory and practice, exploring concepts like machine learning, deep learning, computer vision, AI-integrated applications, IoT-based technologies, healthcare data analytics, and biotechnology applications. Through this, the book empowers healthcare practitioners to pioneer innovative solutions that enhance efficiency, accuracy, and security in medical practices. AI and IoT-Based Technologies for Precision Medicine not only offer insights into the potential of AI-powered applications and IoT-equipped techniques in smart healthcare but also foster collaboration among healthcare scholars and professionals. This authoritative guide encourages knowledge sharing and collaboration to harness the transformative potential of AI and IoT, leading to revolutionary advancements in medical practices and healthcare services. With this book as a guide, readers can navigate the evolving landscape of high-tech medicine, taking confident steps toward a cutting-edge and precise medical ecosystem.

As technology continues to develop, the healthcare industry must adapt and implement new technologies and services. Recent advancements, opportunities, and challenges for bio-medical image processing and authentication in telemedicine must be considered to ensure patients receive the best possible care. Advancements in Bio-Medical Image Processing and Authentication in Telemedicine introduces recent advancements, opportunities, and challenges for bio-medical image processing and authentication in telemedicine and discusses the design of high-accuracy decision support systems. Covering key topics such as artificial intelligence, medical imaging, telemedicine, and technology, this premier reference source is ideal for medical professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

The healthcare industry is starting to adopt digital twins to improve personalized medicine, healthcare organization performance, and new medicine and devices. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations as well as drug and device manufacturers. Digital twins are digital representations of human physiology built on computer models. The use of digital twins in healthcare is revolutionizing clinical processes and hospital management by enhancing medical care with digital tracking and advancing modelling of the human body. These tools are of great help to researchers in studying diseases, new drugs, and medical devices. Digital Twins and Healthcare: Trends, Techniques, and Challenges facilitates the advancement and knowledge dissemination in methodologies and applications of digital twins in the healthcare and medicine fields. This book raises interest and awareness of the uses of digital twins in healthcare in the research community. Covering topics such as deep neural network, edge computing, and transfer learning method, this premier reference source is an essential resource for hospital administrators, pharmacists, medical professionals, IT consultants, students and educators of higher education, librarians, and researchers.

This book offers a comprehensive yet concise overview of the challenges and opportunities presented by the use of artificial intelligence in healthcare. It does so by approaching the topic from multiple perspectives, e.g. the nursing, consumer, medical practitioner, healthcare manager, and data analyst perspective. It covers human factors research, discusses patient safety issues, and addresses ethical challenges, as well as important policy issues. By reporting on cutting-edge research and hands-on experience, the book offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes. It will also benefit students and researchers whose work involves artificial intelligence-related research issues in healthcare.

This book constitutes the refereed proceedings of the first MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR_CBS 2009, held in London, UK, in September 2009. The 10 revised full papers were carefully reviewed and selected from numerous submissions. The papers are divide on several topics on medical image retrieval, clinical decision making and multimodal fusion.

The book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.

This book presents real-world decision support systems, i.e., systems that have been running for some time and as such have been tested in real environments and complex situations; the cases are from various application domains and highlight the best practices in each stage of the system’s life cycle, from the initial requirements analysis and design phases to the final stages of the project. Each chapter provides decision-makers with recommendations and insights into lessons learned so that failures can be avoided and successes repeated. For this reason unsuccessful cases, which at some point of their life cycle were deemed as failures for one reason or another, are also included. All decision support systems are presented in a constructive, coherent and deductive manner to enhance the learning effect. It complements the many works that focus on theoretical aspects or individual module design and development by offering ‘good’ and ‘bad’ practices when developing and using decision support systems. Combining high-quality research with real-world implementations, it is of interest to researchers and professionals in industry alike.