
Clinical Implementation of AI-Driven Neurological Triage
Workflow Integration, Validation Frameworks, and Outcome Optimization
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This book provides an in-depth exploration of how artificial intelligence (AI) is revolutionizing neurological triage and clinical decision-making. It examines the design, validation, and integration of AI-driven workflows into real-world hospital systems, focusing on their capacity to improve diagnostic accuracy, response time, and patient outcomes in acute neurological conditions such as stroke, traumatic brain injury, and epilepsy. Through evidence-based frameworks, the book explains how machine learning algorithms and predictive analytics can prioritize patient risk, optimize imaging inter...
This book provides an in-depth exploration of how artificial intelligence (AI) is revolutionizing neurological triage and clinical decision-making. It examines the design, validation, and integration of AI-driven workflows into real-world hospital systems, focusing on their capacity to improve diagnostic accuracy, response time, and patient outcomes in acute neurological conditions such as stroke, traumatic brain injury, and epilepsy. Through evidence-based frameworks, the book explains how machine learning algorithms and predictive analytics can prioritize patient risk, optimize imaging interpretation, and support clinician decision pathways. It also discusses ethical and regulatory aspects, validation protocols, interoperability challenges, and clinical impact assessment. Bridging neuroscience, informatics, and systems engineering, this book serves as a guide for neurologists, AI researchers, and healthcare administrators aiming to implement and validate intelligent triage systems for outcome optimization.