Revolutionizing Healthcare Systems With Next Generation Technologies The Role Of Artificial Intelligence Cloud Infrastructure And Big Data In Driving Patient Centric Innovation
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Revolutionizing Healthcare Systems with Next-Generation Technologies: The Role of Artificial Intelligence, Cloud Infrastructure, and Big Data in Driving Patient-Centric Innovation
Author: Karthik Chava
language: en
Publisher: Deep Science Publishing
Release Date: 2025-06-06
In the ever-evolving landscape of global healthcare, the convergence of Artificial Intelligence (AI), Cloud Infrastructure, and Big Data is reshaping how care is delivered, diseases are detected, and patient outcomes are improved. This book, Revolutionizing Healthcare Systems with Next-Generation Technologies: The Role of Artificial Intelligence, Cloud Infrastructure, and Big Data in Driving Patient-Centric Innovation, seeks to explore how these transformative technologies are enabling a shift from reactive to proactive, from generalized to personalized, and from fragmented to integrated healthcare systems. The vision for this work emerged from the growing realization that traditional models of care are no longer sufficient in addressing the complexities of modern medicine. Healthcare providers today must respond to an expanding population, an explosion of health data, and an increasing demand for more accessible, affordable, and individualized care. AI-driven diagnostics, predictive analytics powered by big data, and scalable cloud platforms have become essential tools in this transformation—making healthcare not only smarter but also more compassionate and responsive to individual needs. This book is designed for a wide audience—from researchers, practitioners, and policymakers to technology innovators and students. It offers a comprehensive view of how next-gen technologies are being deployed across healthcare sectors including clinical decision-making, hospital operations, disease surveillance, remote patient monitoring, and precision medicine. It highlights successful implementations, emerging challenges, and ethical considerations, while emphasizing the need for a human-centered approach in tech-driven care. As we look toward the future, this work serves as both a roadmap and a call to action—urging stakeholders to embrace innovation, build interoperable systems, and ensure that the benefits of these technologies reach every patient, regardless of geography or income. With thoughtful collaboration between technologists, clinicians, and communities, we believe that the next revolution in healthcare is not only possible—it is already underway.
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.
Artificial Intelligence in Healthcare
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data