Explainable AI for Medical Data by Ganesh R Naik (.ePUB)+
File Size: 70.6 MB
Explainable Artificial Intelligence for Medical Data Analytics and Healthcare Applications by Ganesh R Naik
Requirements: .ePUB, .PDF reader, 70.6 MB | True PDF, True EPUB
Overview: This book discusses Explainable Artificial Intelligence (XAI) and its applications in healthcare, providing a broad overview of state-of-the-art approaches for accurate analysis and diagnosis. It encompasses computational vision processing techniques that handle complex physiological information, electronic healthcare records, and medical imaging data that assist in earlier prediction. This book explores how explainable AI methods provide a solution for the future of medical data analytics precision medicine and highlights the challenges and considerations that must be addressed. This book summarizes and categorize the Explainable AI types and highlight the algorithms used to increase interpretability in medical data and imaging topics. In addition, it focuses on the challenging Explainable AI problems in medical applications and provide guidelines to develop better learning models using Explainable AI concepts in medical image and text analysis. Furthermore, this edited book will provide future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical data/imaging. Explainable AI (XAI) aims to address the inherent opacity of many AI models, often referred to as the “black-box” problem. This problem arises because many advanced AI systems, particularly those based on Deep Learning and other complex algorithms, make decisions that are not easily interpretable by humans. This lack of transparency can hinder the adoption of AI in critical fields such as healthcare, where understanding the decision-making process is crucial for trust and accountability. The black box problem in AI is particularly pronounced in models such as artificial neural networks (ANNs) and unsupervised Machine Learning (ML) algorithms, which process information through layers of interconnected nodes and generate outcomes without revealing underlying decision pathways. This opaqueness and lack of transparency challenge the verification of AI’s reliability and the identification of potential biases or errors in the decision-making process. Various XAI techniques have been developed to mitigate these issues.
Genre: Non-Fiction > Tech & Devices
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