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Explainable Artificial Intelligence in the Healthcare Industry
Abhishek Kumar (Edited by), Kumar (Author), T. Ananth Kumar (Edited by), Prasenjit Das (Edited by), Chetan Sharma (Edited by), Ashutosh Kumar Dubey (Edited by)
9781394249268, Wiley
Hardback, published 28 March 2025
704 pages
22.9 x 15.2 x 4 cm, 1.134 kg
Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in the Healthcare Industry, a must-have resource. Explainable AI (XAI) has significant implications for the healthcare industry, where trust, accountability, and interpretability are crucial factors for the adoption of artificial intelligence. XAI techniques in healthcare aim to provide clear and understandable explanations for AI-driven decisions, helping healthcare professionals, patients, and regulatory bodies to better comprehend and trust the AI models’ outputs. Explainable AI in the Healthcare Industry presents a comprehensive exploration of the critical role of explainable AI in revolutionizing the healthcare industry. With the rapid integration of AI-driven solutions in medical practice, understanding how these models arrive at their decisions is of paramount importance. The book delves into the principles, methodologies, and practical applications of XAI techniques specifically tailored for healthcare settings.
Preface xxix 1 A Review on Explainable Artificial Intelligence for Healthcare 1 2 Explainable Artificial Intelligence (XAI) in Healthcare: Fostering Transparency, Accountability, and Responsible AI Deployment 17 3 Illuminating the Diagnostic Path: Unveiling Explainability in Medical Imaging 39 4 HealsHealthAI: Unveiling Personalized Healthcare Insights with Open Source Fine-Tuned LLM 67 5 Introduction to Explainable AI in EEG Signal Processing: A Review 79 6 Transparency in Disease Diagnosis: Leveraging Interpretable Machine Learning in Healthcare 105 7 Transparency in Text: Unraveling Explainability in Healthcare Natural Language Processing 131 8 Introduction to Explainable AI in Healthcare: Enhancing Transparency and Trust 161 9 Interpretable Machine Learning Techniques 185 10 Interpretable Machine Learning Techniques in AI 209 11 Interpretable Machine Learning Techniques in Medical System—The Role of Data Analytics and Machine Learning 233 12 Interpretable AI: Shedding Light on Medical Image Analysis Using Machine Learning Techniques 257 13 Exploring the Role of Explainable AI in Women’s Health: Challenges and Solutions 283 14 Explainable AI in Healthcare: Introduction 307 15 Ethical Implications of Emotion Recognition Technology in Mental Healthcare: Navigating Privacy, Bias, and Therapeutic Boundaries 325 16 Bridging the Gap: Clinical Adoption and User Perspectives of Explainable AI in Healthcare 349 17 Application of AI-Based Technologies in the Healthcare Sector: Opportunities, Challenges, and Its Impact—Review 375 18 A Complete Road Map for Interpretable Machine Learning Techniques Harnessing Various Real-Time Applications 393 19 Future Research Directions: Explainable Artificial Intelligence in Healthcare Industry 423 20 Real-World Applications of Explainable AI in Healthcare 451 21 Explainable AI in Medical Imaging, Personalized Medicine, and Bias Reduction: A New Era in Healthcare 467 22 Understanding Explainability in Medical Imaging 493 23 Explainability and Regulatory Compliance in Healthcare: Bridging the Gap for Ethical XAI Implementation 521 24 Envisioning Explainable AI: Significance, Real-Time Applications, and Challenges in Healthcare 563 25 Enlightened XAI: Illuminating Ethics and Equitable Explainability 593 26 Enhancing Trust and Collaboration Using Explainability in Natural Language Processing for AI-Driven Healthcare 619 About the Editors 651 Index 653
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Subject Areas: Computer science [UY]
