Ruiling Guo – författare

Healthcare is fundamentally different from other domains where AI has achieved remarkable success. When an AI system recommends a treatment, suggests a diagnosis, or flags a patient for intervention, lives hang in the balance. Healthcare professionals require more than accurate predictions; they need to understand the reasoning behind those predictions. Explainable AI (XAI) provides the transparency necessary to identify and address algorithmic biases that might perpetuate or exacerbate health disparities.This book addresses this critical challenge by exploring the intersection of healthcare informatics and XAI. It brings together diverse perspectives from clinicians, data scientists, ethicists, and healthcare administrators to examine how transparent and interpretable AI systems can enhance medical practice while maintaining the trust and confidence of both healthcare providers and patients. The book not only showcases technological capabilities but also demonstrates how explainability can bridge the gap between AI innovation and clinical reality.Maintaining a balance between technical rigor and practical accessibility, the book presents detailed discussions of explainability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model- Agnostic Explanations), and causal inference methods. Case studies and examples demonstrate how different XAI techniques can be selected and tailored based on specific requirements. The book also addresses critical implementation challenges.At the threshold of AI’s deeper integration into healthcare, the choices made today about transparency and explainability will shape the future of medicine. This book argues that explainability is not a luxury or an afterthought— “it is a fundamental requirement for responsible AI deployment in healthcare."

Healthcare is fundamentally different from other domains where AI has achieved remarkable success. When an AI system recommends a treatment, suggests a diagnosis, or flags a patient for intervention, lives hang in the balance. Healthcare professionals require more than accurate predictions; they need to understand the reasoning behind those predictions. Explainable AI (XAI) provides the transparency necessary to identify and address algorithmic biases that might perpetuate or exacerbate health disparities.This book addresses this critical challenge by exploring the intersection of healthcare informatics and XAI. It brings together diverse perspectives from clinicians, data scientists, ethicists, and healthcare administrators to examine how transparent and interpretable AI systems can enhance medical practice while maintaining the trust and confidence of both healthcare providers and patients. The book not only showcases technological capabilities but also demonstrates how explainability can bridge the gap between AI innovation and clinical reality.Maintaining a balance between technical rigor and practical accessibility, the book presents detailed discussions of explainability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model- Agnostic Explanations), and causal inference methods. Case studies and examples demonstrate how different XAI techniques can be selected and tailored based on specific requirements. The book also addresses critical implementation challenges.At the threshold of AI’s deeper integration into healthcare, the choices made today about transparency and explainability will shape the future of medicine. This book argues that explainability is not a luxury or an afterthought— “it is a fundamental requirement for responsible AI deployment in healthcare."

Healthcare is fundamentally different from other domains where AI has achieved remarkable success. When an AI system recommends a treatment, suggests a diagnosis, or flags a patient for intervention, lives hang in the balance. Healthcare professionals require more than accurate predictions; they need to understand the reasoning behind those predictions. Explainable AI (XAI) provides the transparency necessary to identify and address algorithmic biases that might perpetuate or exacerbate health disparities.This book addresses this critical challenge by exploring the intersection of healthcare informatics and XAI. It brings together diverse perspectives from clinicians, data scientists, ethicists, and healthcare administrators to examine how transparent and interpretable AI systems can enhance medical practice while maintaining the trust and confidence of both healthcare providers and patients. The book not only showcases technological capabilities but also demonstrates how explainability can bridge the gap between AI innovation and clinical reality.Maintaining a balance between technical rigor and practical accessibility, the book presents detailed discussions of explainability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model- Agnostic Explanations), and causal inference methods. Case studies and examples demonstrate how different XAI techniques can be selected and tailored based on specific requirements. The book also addresses critical implementation challenges.At the threshold of AI's deeper integration into healthcare, the choices made today about transparency and explainability will shape the future of medicine. This book argues that explainability is not a luxury or an afterthought- "e;it is a fundamental requirement for responsible AI deployment in healthcare."e;

Healthcare is fundamentally different from other domains where AI has achieved remarkable success. When an AI system recommends a treatment, suggests a diagnosis, or flags a patient for intervention, lives hang in the balance. Healthcare professionals require more than accurate predictions; they need to understand the reasoning behind those predictions. Explainable AI (XAI) provides the transparency necessary to identify and address algorithmic biases that might perpetuate or exacerbate health disparities.This book addresses this critical challenge by exploring the intersection of healthcare informatics and XAI. It brings together diverse perspectives from clinicians, data scientists, ethicists, and healthcare administrators to examine how transparent and interpretable AI systems can enhance medical practice while maintaining the trust and confidence of both healthcare providers and patients. The book not only showcases technological capabilities but also demonstrates how explainability can bridge the gap between AI innovation and clinical reality.Maintaining a balance between technical rigor and practical accessibility, the book presents detailed discussions of explainability techniques, including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model- Agnostic Explanations), and causal inference methods. Case studies and examples demonstrate how different XAI techniques can be selected and tailored based on specific requirements. The book also addresses critical implementation challenges.At the threshold of AI's deeper integration into healthcare, the choices made today about transparency and explainability will shape the future of medicine. This book argues that explainability is not a luxury or an afterthought- "e;it is a fundamental requirement for responsible AI deployment in healthcare."e;

This book explores the complex and rapidly evolving internet of things (IOT) and the expanding role of wearable technologies on healthcare. Remote monitoring and wearable devices have become increasingly popular in recent years, enabling continuous tracking of vital signs, activity levels and other health parameters. This can support early detection of potential health issues, facilitate proactive preventive care and allow healthcare providers to monitor patients' conditions remotely. By integrating data from wearables with electronic health records, it is possible to obtain a comprehensive view of a patient's health status. This book is intended primarily for postgraduate students and researchers, but is also suitable for advanced undergraduates across a range of health‑related disciplines. These include computer science (with an emphasis on healthcare informatics), healthcare informatics, data science, biomedical engineering and medical ethics. It is written for readers interested in the intersection of the medical IOT, healthcare and data security. The book also serves as a valuable resource for medical practitioners, healthcare IT professionals, hospital administrators and clinical data analysts. In addition, it will appeal to AI developers, data scientists, cybersecurity analysts and product managers working in healthcare technology.Advancing Healthcare with the Medical Internet of Things offers an overview of cutting‑edge innovations driving smart, responsive and patient‑focused healthcare. The book highlights both the opportunities these devices create and the technological advancements still needed to fully realize their potential. The chapters explore the ethical and legal implications of collecting, storing and using patient data generated by wearable devices, emphasizing the importance of responsible data governance. The book also focuses on hidden biases embedded in IOT systems and their potential to exacerbate existing healthcare disparities, underscoring the need to identify and mitigate these biases to ensure equitable healthcare delivery. In addition, it describes a range of wearable technologies used across global health systems, assessing their advantages and limitations, and therefore equips healthcare providers, policymakers and technologists with a clearer understanding of both the benefits and the risks associated with integrating wearable devices into clinical practice.