Haipeng Liu – författare

This book provides a comprehensive overview of cutting-edge technologies in the diagnosis of multiple cardiovascular disease. Readers will understand technical advancements and research gaps, which will help them to optimize the design of algorithms and devices.

Cutting-Edge Diagnostic Technologies in Cardiovascular Diseases: Towards Data-Driven Smart Healthcare provides details on the adoption and implementation of advanced diagnostic techniques in different clinical settings, including population screening, clinical diagnosis, risk prediction, data-driven diagnosis, and remote healthcare monitoring. It also covers various cardiovascular diseases, from the macrovascular to microvascular levels, where early and accurate diagnosis is a high clinical need, for example, stroke, intracranial atherosclerosis, coronary artery disease, and microvascular dysfunction. The book is a practical guide with case studies, and the authors cover a wide range of cutting-edge diagnostic techniques, including artificial intelligence, radiological imaging, wearable sensors, genetic biomarkers, and multi-omics data integration. It summarizes the potentials, challenges, and ethical concerns in the implementation of these techniques under current clinical settings. It also discusses the future directions and perspectives for next-generation diagnostics based on AI-enhanced multimodal data fusion.

This book targets a mixed audience of data scientists, engineers, clinicians, researchers, academics, and students interested in cutting-edge technologies, methodologies, and practices in the diagnostics of cardiovascular diseases to resolve the challenging gaps in healthcare and clinical applications.

This book provides a comprehensive overview of cutting-edge technologies in the diagnosis of multiple cardiovascular disease. Readers will understand technical advancements and research gaps, which will help them to optimize the design of algorithms and devices.

Cutting-Edge Diagnostic Technologies in Cardiovascular Diseases: Towards Data-Driven Smart Healthcare provides details on the adoption and implementation of advanced diagnostic techniques in different clinical settings, including population screening, clinical diagnosis, risk prediction, data-driven diagnosis, and remote healthcare monitoring. It also covers various cardiovascular diseases, from the macrovascular to microvascular levels, where early and accurate diagnosis is a high clinical need, for example, stroke, intracranial atherosclerosis, coronary artery disease, and microvascular dysfunction. The book is a practical guide with case studies, and the authors cover a wide range of cutting-edge diagnostic techniques, including artificial intelligence, radiological imaging, wearable sensors, genetic biomarkers, and multi-omics data integration. It summarizes the potentials, challenges, and ethical concerns in the implementation of these techniques under current clinical settings. It also discusses the future directions and perspectives for next-generation diagnostics based on AI-enhanced multimodal data fusion.

This book targets a mixed audience of data scientists, engineers, clinicians, researchers, academics, and students interested in cutting-edge technologies, methodologies, and practices in the diagnostics of cardiovascular diseases to resolve the challenging gaps in healthcare and clinical applications.

This book serves as a critical resource that bridges the gap between burgeoning technology and its practical implementation. The book starts with an in-depth exploration of healthcare 5.0 principles, laying the foundation for the reader to understand the current shifts in healthcare paradigms. Then, it dives into the intricacies of generative models in healthcare, detailing how these algorithms work and the applications they serve. The book further delves into the subsets of generative machine learning and deep learning techniques in healthcare. As we move towards more complex applications, the book takes a turn to address the critical subject of interpretability and explainability in generative models, a topic that resonates profoundly given the life-critical nature of medical decisions. Finally, the book concludes with a robust discussion on the security and privacy concerns that accompany the deployment of GAI in real healthcare settings. By offering a multidimensional viewpoint—coupled with case studies, statistical analyses, and expert insights—the book ensures that the reader is left with a nuanced understanding of how GAI can be both a boon and a challenge in healthcare. As such, the proposed book serves as an indispensable resource for healthcare professionals, data scientists, researchers, and anyone invested in the future of healthcare and AI.