Explainable and Responsible Artificial Intelligence in Healthcare (eBook, ePUB)

Redaktion: Malviya, Rishabha; Sundram, Sonali

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 545
• Erscheinungstermin: 4. März 2025
• Englisch
• ISBN-13: 9781394302420
• Artikelnr.: 73561704

Autorenporträt

Rishabha Malviya, PhD, is an associate professor in the Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University. He has authored more than 150 research/review papers for national/international journals of repute. He has been granted more than 10 patents from different countries while a further 40 patents have either been published or under evaluation. His areas of interest include formulation optimization, nanoformulation, targeted drug delivery, localized drug delivery, and characterization of natural polymers as pharmaceutical excipients. Sonali Sundram, PhD and MPharm, completed her doctorate in pharmacy and is an assistant professor at Galgotias University, Greater Noida. Her areas of interest are neurodegeneration, clinical research, and artificial intelligence. She has edited four books.

Inhaltsangabe

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