shinners lucy (4 risultati)

Paperback. Condizione: new. Paperback. 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 AIs 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." Explainable Artificial Intelligence (XAI) in healthcare is an emerging field focused on making the decisions and processes of AI systems transparent and understandable to humans, particularly healthcare professionals. This book examines the complex and rapidly evolving intersection of healthcare and AI. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condizione: new. Paperback. 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 AIs 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." Explainable Artificial Intelligence (XAI) in healthcare is an emerging field focused on making the decisions and processes of AI systems transparent and understandable to humans, particularly healthcare professionals. This book examines the complex and rapidly evolving intersection of healthcare and AI. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condizione: new. Hardcover. 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 AIs 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." Explainable Artificial Intelligence (XAI) in healthcare is an emerging field focused on making the decisions and processes of AI systems transparent and understandable to humans, particularly healthcare professionals. This book examines the complex and rapidly evolving intersection of healthcare and AI. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condizione: new. Hardcover. 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 AIs 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." Explainable Artificial Intelligence (XAI) in healthcare is an emerging field focused on making the decisions and processes of AI systems transparent and understandable to humans, particularly healthcare professionals. This book examines the complex and rapidly evolving intersection of healthcare and AI. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.