Explainable and Responsible Artificial Intelligence in Healthcare

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.

• Foreword xixPreface xxi1 Uncapping Explainable Artificial Intelligence--Centered Reinforcement Learning and Natural Language Processing in Smart Healthcare System 1Bhupinder Singh, Rishabha Malviya, Christian Kaunert and Sathvik Belagodu Sridhar1.1 Introduction 21.2 XAI-Based Reinforcement Learning in Smart Healthcare Systems 51.3 Natural Language Processing in Smart Healthcare Systems 71.4 Incorporation of XAI-Based RL and NLP 101.5 Synergies Between XAI, RL, and NLP in Healthcare 111.6 Patient Engagement and Care Management in Health Sector: XAI and NLP Methods 131.7 Conclusion and Future Scope--Implications for Healthcare Practice 152 Explainable and Responsible AI in Neuroscience: Cognitive Neurostimulation 27Phool Chandra, Himanshu Sharma and Neetu Sachan2.1 Introduction 282.2 Foundations of Cognitive Neurostimulation 302.3 Cognitive Neurostimulation Techniques 342.4 Explainable AI in Cognitive Neurostimulation 372.5 Responsible Artificial Intelligence in Cognitive Neurostimulation 432.6 Interdisciplinary Collaboration 472.7 Case Studies in Explainable and Responsible AI in Cognitive Neurostimulation 482.8 Future Perspective 492.9 Conclusion 493 Diagnostic and Surgical Uses of Explainable AI (XAI) 65Roja Rani Budha, Saba Wahid A.M. Khan, Tushar Lokhande, G.S.N. Koteswara Rao and Shams Aaghaz3.1 Introduction 683.2 Uncertainty of CNN Model Prediction by Leveraging XAI 693.3 Algorithms of XAI Techniques 703.4 Need for Using XAI 723.5 Scope of AI Surgery 743.6 Limitations and Concerns 803.7 Conclusion and Future Implications for Surgeons and Future Perspective 804 Osteoporosis Risk Assessment and Individualized Feature Analysis Using Interpretable XAI and RAI Techniques 89Shivam Rajput, Rishabha Malviya and Sathvik Belagodu Sridhar4.1 Introduction 904.2 Responsible Artificial Intelligence (RAI) 924.3 Explainable Artificial Intelligence (XAI) 934.4 Key Principles of Explainable Artificial Intelligence (XAI) 944.5 Radiomics, Machine Learning, and Deep Learning 984.6 Diagnosis of Osteoporosis 1004.7 General Workflow of AI-Based BMD Classification in CT 1024.8 Conclusion 1045 Spinal Metastasis--Imaging Using XAI and RAI Techniques 115Arti A. Bagada and Priya V. Patel5.1 Introduction 1165.2 Spinal Metastasis: Need of Artificial Intelligence for Imaging 1195.3 Artificial Intelligence Imaging Using XAI and RAI Technique 123Contents ix5.4 Challenges and Future Directions and Research Needs 1345.5 Conclusion 1346 Explainable Artificial Intelligence and Responsible Artificial Intelligence for Dentistry 145Tamanna Rai, Rishabha Malviya and Sathvik Belagodu Sridhar6.1 Introduction 1456.2 The Scope of AI in Healthcare 1476.3 Responsible Artificial Intelligence (AI) in Dentistry 1486.4 Explainable Artificial Intelligence (XAI) in Dentistry 1496.5 Application of AI in Dentistry 1506.6 Benefits of AI in Dentistry 1556.7 Challenges of AI in Dentistry 1576.8 Conclusion 1577 Explainable Artificial Intelligence Technique in Deep Learning--Based Medical Image Analysis 165Babita Gupta, Rishabha Malviya, Sonali Sundram and Sathvik Belagodu Sridhar7.1 Introduction 1667.2 Deep Learning (DL) in the Analysis of Medical Images 1677.3 Guidelines for Clinical XAI 1687.4 Factors to Examine about the Feasibility and Efficacy of Using the Product in the Clinical Environment 1707.5 Factors to Consider During the Evaluation 1717.6 XAI in Medical Image Analysis 1747.7 Non-Visual XAI Techniques in Medical Imaging 1777.8 Challenges and Future Directions 1787.9 Conclusion 1828 XAI Technique in Deep Learning--Based Medical Image Analysis 191Deepak Kumar, Sejal Porwal, Rishabha Malviya and Sathvik Belagodu Sridhar8.1 Introduction 1928.2 XAI Method in Field of Medical Imaging 1958.3 Application of XAI in Medical Imaging 2008.4 Conclusion 2079 XAI-Enabled Telehealth 217Pankaj Kumar Sharma and Neha Krishnarth9.1 Introduction 2189.2 Significance of Telemedicine 2199.3 Reasonable AI Consciousness (XAI) 2209.4 Simulated Intelligence in Telemedicine 2229.5 Challenges in Executing XAI in Medical Services 2239.6 Clinical Choice Help 2249.7 Patient Observing 2249.8 Medical Services Intercessions 2259.9 The Requirement for Mindful Simulated Intelligence in Medical Care 2259.10 Moral Contemplations in Artificial Intelligence Sending 2269.11 AI (ML) in Artificial Intelligence 2279.12 Strategies for Interpretable AI Models 2319.13 Layer-Wise Relevance Propagation 2329.14 Local Interpretable Model-Agnostic Explanations 2339.15 Partial Dependence Plots (PDPs) 2349.16 Straight Forwardness in Artificial Intelligence Calculations 2369.17 Difficulties of Reasonable Artificial Intelligence Logical 2379.18 Consolidating Computer-Based Intelligence in Medical Services Conveyance 2389.19 Functional Ramifications of XAI in Medical Services Reasonable 2409.20 Available XAI Besides the Costs of Logic 2439.21 Conversation 2439.22 Conclusion 24510 Intelligent Algorithm for Seizure Alignment Using EEG Clustering with Special Reference to Discrete Wavelet Transform Theory 251Pankaj Kalita, Arup Sarmah, Chayanika Devi, Partha Pratim Kalita and Arnabjyoti Deva Sarma10.1 Introduction 25210.2 Different Intelligent/Computational Approaches for Seizure Classification 25310.3 The Architecture of EEG-Specific CNNs 25610.4 Training EEG-Specific CNNs 25710.5 Significance of EEG CNNs 25810.6 Challenges and Future Directions 25810.7 Recurrent Neural Networks 25910.8 Applications in EEG Analysis 26010.9 Ensemble Methods 26110.10 Transfer Learning 26210.11 Seizure EEG Clustering Using Discrete Wavelet Transform Algorithm 26410.12 Present Findings 26710.13 Conclusion 27111 Analysis of Biomedical Data with Explainable (XAI) and Responsive AI (RAI) 277Arjun K.R., Girish Kanavi K., Varshitha B.R., Mythreyi R., Sridhar Muthusami, Nandini G. and Kanthesh M. Basalingappa11.1 Introduction 27911.2 Explainable Artificial Intelligence Modeling for Biomedical Data Analysis Using a Correlation-Based Feature Selection Method 28111.3 Biomedical Data Analysis of Various Diseases: The Functions of XAI and RAI 28311.4 A Comparative Study Between Manual Analysis and Analysis with XAI and RAI 28511.5 Differentiation of AI and XAI/RAI Methods 28611.6 Analyzing Data Using Traditional Methods Versus Using AI can Differ Significantly in Several Aspects 28711.7 Advantages of AI 28711.8 Comparison of AI’s Pros and Cons 28911.9 Future Aspects 29111.10 Conclusion 29312 Classify Chronic Wounds: The Need of Explainable AI and Responsible AI 297Saurav Sarkar, Soma Das, Ananya Chanda and Sayan Biswas12.1 Introduction 29812.2 Understanding Chronic Wounds 30112.3 The Rise of AI in Wound Classification 30412.4 Explainable AI: Unravelling the Black Box 30812.5 Responsible AI in Wound Classification 31112.6 Case Studies and Applications 31312.7 Conclusion 31513 Bone Metastases: Explainable AI and Responsible AI 323Avipsa Hazra, Gowrav Baradwaj, Sushma R., Sudipta Choudhury, Mythreyi R. and Kanthesh B.M.13.1 Introduction to Bone Metastases 32513.2 Traditional Diagnostic and Therapeutic Method for Bone Metastasis 32713.3 AI Involvement in Diagnosis and Therapy of Bone Metastasis 33713.4 Case Studies of Current AI Success in Bone Metastasis 34013.5 Recent Advancements and Future Perspectives 34313.6 Conclusion 345References 345Index 349