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Books in Biomedical engineering

  • Deep Learning Approaches for Healthcare Data Analysis and Decision Making

    • 1st Edition
    • Ashish Bagwari + 5 more
    • English
    Deep Learning Approaches for Healthcare Data Analysis and Decision Making demystifies complex data-driven technologies, providing a clear framework for integrating advanced analytics into healthcare practices. With a focus on practical applications, the authors present a comprehensive digital transformation methodology that empowers readers to tackle the multifaceted challenges of healthcare data management. By leveraging deep learning techniques, readers will learn to analyze vast datasets, identify critical patterns, and develop predictive models that enhance diagnosis and treatment strategies while ensuring compliance with stringent data regulations. The book also addresses the pressing need for ethical AI practices, emphasizing patient privacy and data security. Real-world case studies illustrate how to implement personalized healthcare solutions and foster interdisciplinary collaboration, breaking down silos in knowledge and practice. Moreover, it explores innovative business models for sustainable AI integration, offering actionable insights for healthcare providers. This resource equips professionals with the tools to drive innovation, improve patient outcomes, and navigate the complexities of digital transformation in healthcare, making it a must-read for anyone at the intersection of technology and healthcare.

  • Modern Bioinformatics

    From Molecular Insights to Biomedical Innovation
    • 1st Edition
    • Kolla Bhanu Prakash + 2 more
    • English
    Modern Bioinformatics from Molecular Insights to Biomedical Innovation explores the rapidly advancing field of bioinformatics, addressing a critical gap in current literature by integrating quantum biology and quantum biosensors with computational bioinformatics. As quantum computing and nanotechnology revolutionize biomedical research, this book emerges as an essential resource for researchers navigating these interdisciplinary domains. It caters to the growing need for comprehensive material that bridges molecular biology, computational methods, and cutting-edge innovations, empowering scientists to harness the full potential of bioinformatics in understanding complex biological systems and developing novel diagnostic and therapeutic strategies. Organized into eight thematic sections, the book offers a thorough exploration of bioinformatics from foundational concepts to translational applications. It begins with molecular structures of proteins, DNA, and RNA, advancing through nano-molecular visualization and structural domain analysis. Readers gain insights into deep learning techniques like AlphaFold for protein structure prediction, AI applications in genomics, and molecular interactions critical to function. The text further covers computational methods in drug discovery, forensic bioinformatics, and molecular evolution. A unique emphasis is placed on the convergence of nanotechnology and quantum innovations in molecular diagnostics and biosensing, including quantum biosensors and quantum computing applications. The final sections focus on computational strategies for drug target identification, biomarker discovery, and clinical trial analysis, providing a holistic view of modern bioinformatics workflows. This book is an indispensable reference for advanced researchers, graduate students, and professionals in bioinformatics, molecular biology, and biomedical engineering seeking to integrate quantum computing and nanotechnology into their work.

  • Integrating AI in Psychological and Mental Health Care

    Techniques, Applications, and Ethical Considerations
    • 1st Edition
    • Sandeep Kautish + 4 more
    • English
    Integrating AI in Psychological and Mental Health Care: Techniques, Applications, and Ethical Considerations introduces key concepts and the historical evolution of AI, providing a foundation for understanding its applications in mental health. The content delves into various aspects of AI, including diagnostic tools, machine learning algorithms, and natural language processing, highlighting their roles in enhancing therapeutic outcomes and improving patient care. The discussion encompasses significant mental health conditions such as anxiety, depression, and severe psychological disorders, showcasing how AI technologies can assist in diagnosis, treatment planning, and monitoring. Ethical considerations and privacy issues are critically examined, ensuring a balanced perspective on the benefits and challenges associated with AI-driven interventions. Practical applications, such as virtual psychotherapists and AI-enhanced cognitive behavioral therapy illustrate real-world implementations and their impact on patient care. Additionally, case studies provide insights into successful AI applications in mental health settings, thus enhancing our understanding of potential advantages and obstacles.

  • Advances in Medical Imaging

    From Behavioral Analysis to Disease Prevention and Rehabilitation
    • 1st Edition
    • Dilber Uzun Ozsahin + 4 more
    • English
    Advances in Medical Imaging: From Behavioral Analysis to Disease Prevention and Rehabilitation presents a comprehensive exploration of the rapidly evolving field of medical imaging at the crossroads of clinical science, behavioral research, and public health. This book addresses the growing need to understand how advanced imaging technologies—such as MRI, PET, and ultrasound—are transforming healthcare beyond traditional diagnosis. By capturing structural, functional, metabolic, and molecular processes, these innovations provide new insights into brain function, lifestyle impacts, chronic disease, and rehabilitation, supporting the shift toward personalized and preventive medicine.The book is organized into five thematic sections covering a wide range of topics. Early chapters focus on the integration of neuroimaging with behavioral science, highlighting brain–behavior relationships through cutting-edge imaging modalities and cognitive software. Subsequent sections examine the effects of sedentary lifestyles on brain health, vascular function assessed by ultrasound, and musculoskeletal aging through imaging biomarkers. Additional chapters explore chronic disease management, yoga and cognitive well-being, stroke recovery, traumatic brain injury rehabilitation, and the expanding role of imaging in oncology and systemic diseases. Throughout, the book emphasizes the use of artificial intelligence, radiomics, and multimodal imaging to enhance precision medicine and population health research.Advances in Medical Imaging: From Behavioral Analysis to Disease Prevention and Rehabilitation offers valuable insights for researchers, clinicians, and healthcare professionals interested in the applications of medical imaging across multiple disciplines. It highlights imaging’s critical role in advancing personalized care, supporting preventive strategies, and fostering innovation in modern healthcare.

  • Humanizing Artificial Touch

    Skin, Sensing, and Structures
    • 1st Edition
    • John-John Cabibihan
    • English
    Humanizing Artificial Touch: Skin, Sensing, and Structures contains recent advances in artificial skin modelling and robotic tactile sensing technologies, integrating these in the structure of an artificial hand mimicking its important features. This book will give special treatment to the relationship between the artificial skin material and embedded tactile sensors, describing how artificial skin and embedded sensors can be designed and constructed using 3D printing technologies with case examples on how these were created for amputees achieve a more a natural and rich human-robot tactile interaction.This book is oriented towards students, researchers, and clinical practitioners who are working in the design and construction of artificial hands for prosthetics and social robotics.

  • Self-Learning AI in Healthcare

    Agentic Systems for Smarter Medicine
    • 1st Edition
    • Rajesh Kumar Dhanaraj + 4 more
    • English
    Self-Learning AI in Healthcare: Agentic Systems for Smarter Medicine introduces an essential and timely exploration into the transformative potential of advanced artificial intelligence within modern medicine. As healthcare faces mounting challenges—from managing vast, complex patient data to improving diagnostic precision and personalizing treatments—tradition... AI models often fall short due to their static nature and dependence on human retraining. This book addresses the critical need for self-learning and agentic AI systems that autonomously adapt, refine decision-making, and navigate complex clinical environments with minimal intervention. By bridging cutting-edge AI research with practical healthcare applications, it opens new pathways toward more intelligent, efficient, and responsive patient care. The book’s comprehensive contents, contributed by leading global experts, span a wide range of pivotal topics. It begins with foundational insights into the rise of self-learning AI and neural networks tailored for adaptive medical systems. Subsequent chapters delve into unsupervised, semi-supervised, and reinforcement learning for autonomous healthcare decision-making, alongside decentralized edge AI approaches. Specialized sections cover personalized medicine, hospital workflow optimization, remote patient monitoring, early disease detection, federated learning for privacy preservation, and AI-driven rehabilitation. Further, this book explores AI applications in drug discovery, mental health support, radiology, digital twins, and medical robotics, culminating with an examination of future challenges, ethics, and regulatory frameworks shaping self-learning AI’s trajectory in healthcare. This book is tailored to serve a diverse yet specialized audience spanning academic, professional, and research sectors. Healthcare IT professionals and clinical informatics specialists will gain practical guidance for implementing adaptive AI solutions within complex healthcare environments. AI researchers and data scientists focused on developing self-learning models will find cutting-edge methodologies and case studies that advance medical applications. Biomedical engineers seeking to integrate autonomous AI systems into medical devices and workflows will benefit from in-depth explorations of real-world innovations. Additionally, graduate and doctoral students in computer science, biomedical informatics, and health data science will acquire comprehensive knowledge essential for mastering the complexities of adaptive AI in healthcare.

  • AI-Driven Diagnostics for 6G-Enabled Smart Healthcare

    • 1st Edition
    • Sangeeta Kumari + 3 more
    • English
    AI-Driven Diagnostics for 6G-Enabled Smart Healthcare explores the transformative integration of artificial intelligence (AI) and next-generation 6G networks in the healthcare sector. The book begins by highlighting the evolution of healthcare technology and the critical role of AI-driven diagnostics, emphasizing how 6G facilitates real-time, ultra-reliable communication. Key features of 6G, such as ultra-low latency and massive connectivity, are discussed, showcasing their impact on advanced healthcare applications like remote diagnostics and patient monitoring. The integration of AI in medical diagnostics is examined, focusing on machine learning and deep learning techniques that enhance disease detection through medical imaging and clinical data analysis. The book explores the benefits of remote patient monitoring, particularly for underserved populations, and explores edge AI for localized, low-latency diagnostic processing. Real-time imaging diagnostics are highlighted, demonstrating how 6G supports rapid transfer and analysis of high-resolution medical images. It also addresses predictive analytics, detailing AI models that forecast diseases and the role of IoT devices and wearables in healthcare diagnostics. Concepts of smart hospitals and the integration of blockchain technology for security and data integrity are discussed. Ethical considerations and regulatory challenges are thoroughly examined, ensuring patient privacy and compliance. The book concludes with insights into future trends and emerging technologies in AI diagnostics, including quantum AI and next-generation sensors.

  • Innovations and Human Factors Challenges in Healthcare Product Design

    • 1st Edition
    • Kamalpreet Sandhu + 3 more
    • English
    Innovations and Human Factors Challenges in Healthcare Product Design covers best practices and advanced design methods to capture, document, and validate various challenges at the early stage of the design process, also delving into how to mitigate design strategies to address those challenges without compromising the cost, effort, safety, and quality of the device. The book provides a comprehensive collection of technical notes, research designs, design methods and processes, case studies, comprehensive literature on the industrial impact of design methods and processes, and applications of digital technologies for design, analysis, manufacturing, testing, and deployment. Readers will find multidisciplinary coverage of the application of technology and science for product design in healthcare sectors.

  • Generative Artificial Intelligence for Neuroimaging

    Methods and Applications
    • 1st Edition
    • Deepika Koundal + 1 more
    • English
    Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. This book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare. It addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.

  • Intelligent Semantic Analysis for Healthcare

    • 1st Edition
    • Sonali Vyas + 3 more
    • English
    Intelligent Semantic Analysis for Healthcare explores the latest trends, developments, and future directions of intelligent semantic analysis techniques on retrieving and managing meaningful medical information for healthcare information systems. The book explores different computational methods, ideas, strategies, and techniques to analyze relevant healthcare information in an innovative and efficient way, thus bridging the gap between gathering and comprehending data with healthcare and biological applications. It offers a comprehensive view of intelligent semantic analysis in healthcare, bridging the gap between data collection and healthcare applications, and providing innovative computational methods for data analysis.Sections focus on intelligent semantic analysis rather than broader topics of big data and healthcare analytics. Additionally, the book is geared towards practical approaches and innovative techniques for state-of-the-art and current challenges in healthcare data management.