Next Frontier Medical Devices And Embedded Systems Harnessing Biomedical Engineering Artificial Intelligence And Cloud Powered Big Data Analytics For Smarter Healthcare Solutions
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Next-Frontier Medical Devices and Embedded Systems: Harnessing Biomedical Engineering, Artificial Intelligence, and Cloud-Powered Big Data Analytics for Smarter Healthcare Solutions
Author: Sai Teja Nuka
language: en
Publisher: Deep Science Publishing
Release Date: 2025-06-06
The intersection of biomedical engineering, artificial intelligence, and cloud-powered big data analytics marks a pivotal moment in the evolution of modern healthcare. Next-Frontier Medical Devices and Embedded Systems: Harnessing Biomedical Engineering, Artificial Intelligence, and Cloud-Powered Big Data Analytics for Smarter Healthcare Solutions is a timely exploration into how these cutting-edge technologies are converging to transform patient care, medical diagnostics, and therapeutic delivery. In an age where real-time data, personalized treatment, and intelligent automation are becoming the norm, the role of smart medical devices and embedded systems has never been more critical. These innovations are not only enhancing the precision and efficiency of clinical operations but also bringing care closer to the patient—through wearable monitors, implantable sensors, and AI-enabled diagnostic tools that function seamlessly in both hospital and home environments. This book is born out of the recognition that future-ready healthcare systems will rely heavily on adaptive, intelligent technologies that are both secure and scalable. Biomedical engineers, data scientists, clinicians, and healthcare technologists are now working in tandem to design solutions that are deeply integrated, data-driven, and focused on preventive and personalized care. The chapters herein reflect this collaboration—providing a multidisciplinary perspective on the design, deployment, and societal impact of next-generation medical systems. Whether you are a researcher, practitioner, policy leader, or student, this book offers critical insights into the challenges, breakthroughs, and ethical dimensions of embedding intelligence into healthcare hardware. From AI-driven surgical tools and diagnostic algorithms to cloud-enabled analytics and edge computing in critical care—this work offers a comprehensive guide to the technological shift redefining healthcare at its core. We hope this book serves not only as a knowledge resource but also as an inspiration to those driving innovation at the frontier of medicine and technology.
Ethical Considerations and Bias Detection in Artificial Intelligence/Machine Learning Applications
Author: Jayesh Rane
language: en
Publisher: Deep Science Publishing
Release Date: 2025-07-10
At a time when artificial intelligence (AI) and machine learning (ML) are used to make sensitive societal decisions such as the ones related to criminal justice, healthcare, finance, education, employment, algorithmic fairness and bias mitigation are among the most important but challenging issues at hand. The goal of this book is to provide a holistic view across various disciplines of the ethical base, detection methods, and technical measures for trustworthy AI systems. Starting from a solid foundation of statistical bias, transparency systems and fairness-aware ML models, this book methodically looks at state-of-the-art methodologies, where we highlight their shortcomings and introduce a unified model framework for detecting bias and transparent algorithms. Moving beyond technical diagnoses, it examines key sociotechnical and policy tools that are required to implement AI responsibly, providing guidance to researchers, engineers, policy makers, and organizational leaders. Literature review has been driven following the experimental case, the fairness trade-offs, intersectional bias, explainability and regulatory compliance are discussed in depth by the authors. This work underscores that fairness in automated decision-making systems depends not only on algorithmic accuracy, but also institutional will and stakeholder engagement. The chapters in this book function as both an academic primer and a resourceful handbook, transitioning readers through an ever-growing ethical AI terrain. Whether you are a data scientist building and deploying an algorithm that encourages ethical speech, or a regulator working to create and refine guidelines around such algorithms, this book provides you with both the tools and the understanding you need for ethical technology development and deployment.
Predictive Intelligence in Biomedical and Health Informatics
Author: Rajshree Srivastava
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2020-10-12
Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.