Explainable AI in Health Informatics by Rajanikanth Aluvalu Hardcover Book
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Explainable AI in Health Informatics by Rajanikanth Aluvalu: A Comprehensive Guide
Artificial Intelligence (AI) has revolutionized the healthcare industry, with applications ranging from personalized medicine to predictive analytics. However, the lack of transparency in AI models has raised concerns about their reliability and accountability. In his groundbreaking book, “Explainable AI in Health Informatics,” Rajanikanth Aluvalu delves into the concept of explainable AI and its implications for healthcare.
Aluvalu begins by providing a thorough overview of AI and its role in health informatics. He then explores the challenges of traditional black-box AI models, which make it difficult for healthcare professionals to understand how decisions are made. Aluvalu argues that explainable AI, which provides insights into how AI models arrive at their conclusions, is essential for ensuring trust and acceptance among stakeholders.
Through case studies and real-world examples, Aluvalu demonstrates how explainable AI can improve clinical decision-making, enhance patient outcomes, and reduce bias in healthcare algorithms. He also discusses the technical aspects of explainable AI, such as model interpretability and feature importance, making the book accessible to both healthcare professionals and data scientists.
“Explainable AI in Health Informatics” is a must-read for anyone interested in the intersection of AI and healthcare. Aluvalu’s insightful analysis and practical recommendations make this book an invaluable resource for understanding the potential of explainable AI to transform the future of healthcare. Whether you’re a healthcare provider, researcher, or policymaker, this book will arm you with the knowledge and tools needed to navigate the complex landscape of AI in health informatics.
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