Explainable and Responsible Artificial Intelligence in Healthcare

This book presents the fundamentals of explainable artificial intelligence (XAI) and responsible artificial intelligence (RAI), discussing their potential to enhance diagnosis, treatment, and patient outcomes. This book explores the transformative potential of explainable artificial intelligence (XAI) and responsible AI (RAI) in healthcare. It provides a roadmap for navigating the complexities of healthcare-based AI while prioritizing patient safety and well-being. The content is structured to highlight topics on smart health systems, neuroscience, diagnostic imaging, and telehealth. The book emphasizes personalized treatment and improved patient outcomes in various medical fields. In addition, this book discusses osteoporosis risk, neurological treatment, and bone metastases. Each chapter provides a distinct viewpoint on how XAI and RAI approaches can help healthcare practitioners increase diagnosis accuracy, optimize treatment plans, and improve patient outcomes. Readers will find the book: explains recent XAI and RAI breakthroughs in the healthcare system; discusses essential architecture with computational advances ranging from medical imaging to disease diagnosis; covers the latest developments and applications of XAI and RAI-based disease management applications; demonstrates how XAI and RAI can be utilized in healthcare and what problems the technology faces in the future. Audience The main audience for this book is targeted to scientists, healthcare professionals, biomedical industries, hospital management, engineers, and IT professionals interested in using AI to improve human health.

Table of Contents:
Foreword xix Preface xxi 1 Uncapping Explainable Artificial Intelligence--Centered Reinforcement Learning and Natural Language Processing in Smart Healthcare System 1 Bhupinder Singh, Rishabha Malviya, Christian Kaunert and Sathvik Belagodu Sridhar 1.1 Introduction 2 1.2 XAI-Based Reinforcement Learning in Smart Healthcare Systems 5 1.3 Natural Language Processing in Smart Healthcare Systems 7 1.4 Incorporation of XAI-Based RL and NLP 10 1.5 Synergies Between XAI, RL, and NLP in Healthcare 11 1.6 Patient Engagement and Care Management in Health Sector: XAI and NLP Methods 13 1.7 Conclusion and Future Scope--Implications for Healthcare Practice 15 2 Explainable and Responsible AI in Neuroscience: Cognitive Neurostimulation 27 Phool Chandra, Himanshu Sharma and Neetu Sachan 2.1 Introduction 28 2.2 Foundations of Cognitive Neurostimulation 30 2.3 Cognitive Neurostimulation Techniques 34 2.4 Explainable AI in Cognitive Neurostimulation 37 2.5 Responsible Artificial Intelligence in Cognitive Neurostimulation 43 2.6 Interdisciplinary Collaboration 47 2.7 Case Studies in Explainable and Responsible AI in Cognitive Neurostimulation 48 2.8 Future Perspective 49 2.9 Conclusion 49 3 Diagnostic and Surgical Uses of Explainable AI (XAI) 65 Roja Rani Budha, Saba Wahid A.M. Khan, Tushar Lokhande, G.S.N. Koteswara Rao and Shams Aaghaz 3.1 Introduction 68 3.2 Uncertainty of CNN Model Prediction by Leveraging XAI 69 3.3 Algorithms of XAI Techniques 70 3.4 Need for Using XAI 72 3.5 Scope of AI Surgery 74 3.6 Limitations and Concerns 80 3.7 Conclusion and Future Implications for Surgeons and Future Perspective 80 4 Osteoporosis Risk Assessment and Individualized Feature Analysis Using Interpretable XAI and RAI Techniques 89 Shivam Rajput, Rishabha Malviya and Sathvik Belagodu Sridhar 4.1 Introduction 90 4.2 Responsible Artificial Intelligence (RAI) 92 4.3 Explainable Artificial Intelligence (XAI) 93 4.4 Key Principles of Explainable Artificial Intelligence (XAI) 94 4.5 Radiomics, Machine Learning, and Deep Learning 98 4.6 Diagnosis of Osteoporosis 100 4.7 General Workflow of AI-Based BMD Classification in CT 102 4.8 Conclusion 104 5 Spinal Metastasis--Imaging Using XAI and RAI Techniques 115 Arti A. Bagada and Priya V. Patel 5.1 Introduction 116 5.2 Spinal Metastasis: Need of Artificial Intelligence for Imaging 119 5.3 Artificial Intelligence Imaging Using XAI and RAI Technique 123Contents ix 5.4 Challenges and Future Directions and Research Needs 134 5.5 Conclusion 134 6 Explainable Artificial Intelligence and Responsible Artificial Intelligence for Dentistry 145 Tamanna Rai, Rishabha Malviya and Sathvik Belagodu Sridhar 6.1 Introduction 145 6.2 The Scope of AI in Healthcare 147 6.3 Responsible Artificial Intelligence (AI) in Dentistry 148 6.4 Explainable Artificial Intelligence (XAI) in Dentistry 149 6.5 Application of AI in Dentistry 150 6.6 Benefits of AI in Dentistry 155 6.7 Challenges of AI in Dentistry 157 6.8 Conclusion 157 7 Explainable Artificial Intelligence Technique in Deep Learning--Based Medical Image Analysis 165 Babita Gupta, Rishabha Malviya, Sonali Sundram and Sathvik Belagodu Sridhar 7.1 Introduction 166 7.2 Deep Learning (DL) in the Analysis of Medical Images 167 7.3 Guidelines for Clinical XAI 168 7.4 Factors to Examine about the Feasibility and Efficacy of Using the Product in the Clinical Environment 170 7.5 Factors to Consider During the Evaluation 171 7.6 XAI in Medical Image Analysis 174 7.7 Non-Visual XAI Techniques in Medical Imaging 177 7.8 Challenges and Future Directions 178 7.9 Conclusion 182 8 XAI Technique in Deep Learning--Based Medical Image Analysis 191 Deepak Kumar, Sejal Porwal, Rishabha Malviya and Sathvik Belagodu Sridhar 8.1 Introduction 192 8.2 XAI Method in Field of Medical Imaging 195 8.3 Application of XAI in Medical Imaging 200 8.4 Conclusion 207 9 XAI-Enabled Telehealth 217 Pankaj Kumar Sharma and Neha Krishnarth 9.1 Introduction 218 9.2 Significance of Telemedicine 219 9.3 Reasonable AI Consciousness (XAI) 220 9.4 Simulated Intelligence in Telemedicine 222 9.5 Challenges in Executing XAI in Medical Services 223 9.6 Clinical Choice Help 224 9.7 Patient Observing 224 9.8 Medical Services Intercessions 225 9.9 The Requirement for Mindful Simulated Intelligence in Medical Care 225 9.10 Moral Contemplations in Artificial Intelligence Sending 226 9.11 AI (ML) in Artificial Intelligence 227 9.12 Strategies for Interpretable AI Models 231 9.13 Layer-Wise Relevance Propagation 232 9.14 Local Interpretable Model-Agnostic Explanations 233 9.15 Partial Dependence Plots (PDPs) 234 9.16 Straight Forwardness in Artificial Intelligence Calculations 236 9.17 Difficulties of Reasonable Artificial Intelligence Logical 237 9.18 Consolidating Computer-Based Intelligence in Medical Services Conveyance 238 9.19 Functional Ramifications of XAI in Medical Services Reasonable 240 9.20 Available XAI Besides the Costs of Logic 243 9.21 Conversation 243 9.22 Conclusion 245 10 Intelligent Algorithm for Seizure Alignment Using EEG Clustering with Special Reference to Discrete Wavelet Transform Theory 251 Pankaj Kalita, Arup Sarmah, Chayanika Devi, Partha Pratim Kalita and Arnabjyoti Deva Sarma 10.1 Introduction 252 10.2 Different Intelligent/Computational Approaches for Seizure Classification 253 10.3 The Architecture of EEG-Specific CNNs 256 10.4 Training EEG-Specific CNNs 257 10.5 Significance of EEG CNNs 258 10.6 Challenges and Future Directions 258 10.7 Recurrent Neural Networks 259 10.8 Applications in EEG Analysis 260 10.9 Ensemble Methods 261 10.10 Transfer Learning 262 10.11 Seizure EEG Clustering Using Discrete Wavelet Transform Algorithm 264 10.12 Present Findings 267 10.13 Conclusion 271 11 Analysis of Biomedical Data with Explainable (XAI) and Responsive AI (RAI) 277 Arjun K.R., Girish Kanavi K., Varshitha B.R., Mythreyi R., Sridhar Muthusami, Nandini G. and Kanthesh M. Basalingappa 11.1 Introduction 279 11.2 Explainable Artificial Intelligence Modeling for Biomedical Data Analysis Using a Correlation-Based Feature Selection Method 281 11.3 Biomedical Data Analysis of Various Diseases: The Functions of XAI and RAI 283 11.4 A Comparative Study Between Manual Analysis and Analysis with XAI and RAI 285 11.5 Differentiation of AI and XAI/RAI Methods 286 11.6 Analyzing Data Using Traditional Methods Versus Using AI can Differ Significantly in Several Aspects 287 11.7 Advantages of AI 287 11.8 Comparison of AI’s Pros and Cons 289 11.9 Future Aspects 291 11.10 Conclusion 293 12 Classify Chronic Wounds: The Need of Explainable AI and Responsible AI 297 Saurav Sarkar, Soma Das, Ananya Chanda and Sayan Biswas 12.1 Introduction 298 12.2 Understanding Chronic Wounds 301 12.3 The Rise of AI in Wound Classification 304 12.4 Explainable AI: Unravelling the Black Box 308 12.5 Responsible AI in Wound Classification 311 12.6 Case Studies and Applications 313 12.7 Conclusion 315 13 Bone Metastases: Explainable AI and Responsible AI 323 Avipsa Hazra, Gowrav Baradwaj, Sushma R., Sudipta Choudhury, Mythreyi R. and Kanthesh B.M. 13.1 Introduction to Bone Metastases 325 13.2 Traditional Diagnostic and Therapeutic Method for Bone Metastasis 327 13.3 AI Involvement in Diagnosis and Therapy of Bone Metastasis 337 13.4 Case Studies of Current AI Success in Bone Metastasis 340 13.5 Recent Advancements and Future Perspectives 343 13.6 Conclusion 345 References 345 Index 349