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Explainable Artificial Intelligence in the Healthcare Industry

Edited by Abhishek Kumar, T. Ananth Kumar, Prasenjit Das, Chetan Sharma, and Ashutosh Kumar Dubey
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394249268  |  Hardcover  |  
692 pages
Price: $225 USD
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One Line Description
Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in Healthcare, a must-have resource for anyone looking to navigate the future of medical practices with confidence.

Audience
Healthcare professionals, administrators, policymakers, innovators, and executives, medical researchers, data scientists and AI engineers, AI ethics experts, patient advocates, pharmaceutical industry professionals, and academic researchers and students seeking to explore the latest advancements and practical applications of explainable AI in the medical field

Description
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 Healthcare 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.

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Author / Editor Details
Abhishek Kumar, PhD is an Assistant Director and associate professor in the Computer Science and Engineering Department at Chandigarh University, India with over 11 years of teaching experience. He has over 100 publications in reputed, peer-reviewed national and international journals, books, and conferences, six internationally published book, and has edited over 27 books. Additionally, he has been a session chair and keynote speaker for many international conferences and webinars in India and abroad.

T. Ananth Kumar, PhD is an associate professor at the Indo-French Educational Trust College of Engineering. He has presented papers in various national and international conferences and journals, as well as published many book chapters. His fields of interest include networks on chips, computer architecture, and application-specific integrated circuit design.

Prasenjit Das, PhD is a professor in the Department of Computer Science and Engineering at Chandigarh University, Punja, India with over 19 years of experience in academics and the IT industry. He has more than 20 research papers and two books to his credit and has filed more than 25 patents, three of which have been granted. Apart from data mining, his other areas of research include machine learning, image processing, and natural language processing.

Chetan Sharma is a program manager at the upGrad Campus, upGradEducation Private Limited, India with more than 15 years of experience in academics, administration, and EdTech. He has published more than 42 research manuscripts in various national and international journals and conferences and presented papers at several national and international conferences. In addition to this, he serves as a reviewer for various journals and conferences and filed more than 30 patents, eight of which have been granted by the Indian Patent Office.

Ashutosh Kumar Dubey, PhD is an associate professor in the Department of Computer Science, School of Engineering and Technology, Chitkara University, India with more than 14 years of teaching experience. He is also a postdoctoral fellow for the Ingenium Research Group Lab, Universidad de Castilla-La Mancha, Ciudad Real, Spain. He has authored and edited ten books and published over 50 articles in peer-reviewed international journals and conference proceedings. Additionally, he is a senior member of the Institute for Electronics and Electrical Engineers and Association for Computing Machinery, as well as an editor editorial board member, and reviewer for many peer-reviewed journals.

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Table of Contents
Preface
1. A Review on Explainable Artificial Intelligence for Healthcare
Rakhi Chauhan
1.1 Introduction
1.2 Literature Review
1.3 Reason for Using XAI
1.3.1 Applications of XAI Algorithms in Different Scenarios
1.3.2 Discussion on Different Aspects of XAI
1.4 Challenges and Future Prospects
1.5 Conclusion
References
2. Explainable Artificial Intelligence (XAI) in Healthcare: Fostering Transparency, Accountability, and Responsible AI Deployment
Asha S. Manek, Shruti Vashist, Geeta Tripathi and Savita Sindhu
2.1 Introduction
2.2 Roles of XAI
2.3 Why XAI
2.4 XAI in Different Sectors
2.5 XAI in Healthcare
2.6 Challenges of XAI Adoption
2.7 Models Used in XAI Adoption
2.8 Conclusion
2.9 Future of XAI in Healthcare
References
3. Illuminating the Diagnostic Path: Unveiling Explainability in Medical Imaging
Sivanantham S., Anwar Basha H., Thanuja K., Shafiya Banu M., Maithili K. and AnilKumar Ambore
3.1 Introduction
3.2 The Need for Explainability in Medical Imaging AI
3.3 Related Works
3.4 Explainable AI Techniques for Medical Imaging
3.4.1 Saliency Maps
3.4.2 Attention Mechanisms
3.4.3 Decision Trees
3.4.4 Gradient-Weighted Class Activation Mapping (Grad-CAM)
3.4.5 LIME (Local Interpretable Model—Agnostic Explanations)
3.4.6 Integrated Gradients
3.4.7 SHAP (SHapley Additive exPlanations)
3.4.8 Layer-Wise Relevance Propagation (LRP)
3.4.9 SmoothGrad
3.4.10 Occlusion Sensitivity
3.4.11 Feature Visualization
3.4.12 Attention Condensation
3.4.13 Counterfactual Explanations
3.5 Real-World Applications and Case Studies
3.6 Deep Learning Approaches for Pneumonia Identification in Chest X-Ray Images: Methods and Methodology
3.6.1 Dataset Collection and Preprocessing
3.6.2 Deep Learning Model Selection
3.6.3 VGG16
3.6.4 Inception V3
3.6.5 ResNet 50V2
3.7 Model Training and Evaluation
3.8 Performance Evaluation
3.9 Comparative Analysis
3.9.1 Implications for Regulatory Compliance, Patient Safety, and Ethics
3.9.2 Regulatory Compliance
3.9.3 Patient Safety
3.10 Ethical Considerations
3.11 Advancing Clinical Workflows with Explainable AI
3.12 Conclusion
References
4. HealsHealthAI: Unveiling Personalized Healthcare Insights with Open Source Fine-Tuned LLM
Lavan J. V. and Lakshmi Sangeetha
4.1 Introduction
4.1.1 HealsHealthAI Objective
4.2 Review Analysis
4.2.1 Literature Review
4.2.2 Additional Review
4.3 Motivation
4.3.1 Moto
4.4 Methodology
4.4.1 Data Collection and Processing
4.4.2 Storage
4.4.3 User Interaction
4.4.4 Model Creation and Loading
4.5 How LLMs, FAISS, and Langchain are Utilized in HealsHealthAI
4.5.1 Personalized Insights
4.5.2 Langchain in HealsHealthAI
4.6 Model Overview and Working
4.6.1 Overall Flowchart
4.6.2 Showcases of HealsHealthAI’s Functionality
4.7 Community Contribution
4.7.1 The Call for Contributions
4.8 Experimental Findings
4.8.1 Quantitative Performance Metrics
4.8.2 Results
4.8.3 Scalability and Resource Usage
4.9 Challenges and Future Paths
4.9.1 Known Issues
4.9.2 Deployment Without Internet Connectivity
4.10 Conclusion
References
5. Introduction to Explainable AI in EEG Signal Processing: A Review
Parag Puranik and Rahul Pethe
5.1 Introduction
5.2 Solution Approaches
5.2.1 Survey of Machine Learning for EEG Signal Processing
5.2.2 DL Procedures for EEG Signal Processing
5.2.2.1 Neural Network for EEG
5.2.3 Feature Extraction and Pre-Processing for EE
5.3 Classification Models for EEG Signals
5.4 Results and Discussions
5.5 Future Scope and Issues
5.6 Proposed Methodology
5.7 Conclusions
References
6. Transparency in Disease Diagnosis: Leveraging Interpretable Machine Learning in Healthcare
Inam Ul Haq, Adil Husain Rather, Syed Zoofa Rufai, Ahmad Shah, Sheetal and Akib Mohi Ud Din Khanday
6.1 Introduction
6.2 Introduction to the Overarching Theme of Transparency and Accountability in AI-Driven Healthcare Solutions
6.3 The Significance of Model Interpretability in Clinical Decision Making and Patient Management Within the Context of Disease Diagnosis
6.4 Importance of Model Interpretability
6.5 Examination of How Interpretable ML Models Facilitate Clearer Understanding and Interpretation of Diagnostic Decisions
6.6 Importance of Transparency in Ensuring Accountability and Fostering Acceptance of AI Technologies Among Healthcare Professionals and Patients
6.7 Techniques for Interpretable Machine Learning
6.8 Explanation of How Each Technique Contributes to Enhancing Diagnostic Accuracy While Providing Transparent Decision Rationale
6.9 Comparative Analysis of Different Interpretable ML Approaches in Terms of Their Applicability and Effectiveness in Disease Diagnosis
6.10 Case Studies and Applications
6.11 Examination of How Interpretable ML has been Utilized to Improve Disease Diagnosis, Treatment Planning, and Patient Outcomes
6.12 Analysis of the Benefits and Challenges Associated with Implementing Interpretable ML in Clinical Practice Through Case Examples and Empirical Evidence
6.13 Conclusion
References
7. Transparency in Text: Unraveling Explainability in Healthcare Natural Language Processing
Madhan Veeramani, Karthick P., S. Venkateswaran, Sriman B., Shaik Thasleem Bhanu and V. Seedha Devi
7.1 Introduction
7.2 Research Objectives
7.2.1 The Promise of NLP in Healthcare
7.2.2 Challenges in NLP Models in Healthcare
7.2.3 The Importance of Transparency and Explainability
7.3 Current Research Landscape
7.4 Related Works
7.5 Literature Survey
7.6 The Role of Explainability in Healthcare NLP
7.6.1 Defining Explainability in Healthcare NLP
7.6.2 Limitations of Traditional Black-Box NLP Models
7.6.3 Benefits of Transparent and Interpretable NLP Models
7.7 Enhancing Clinical Documentation with Interpretable NLP
7.7.1 Methodology
7.7.2 System Development
7.7.3 Integration of Explainable AI Techniques
7.7.3.1 Steps
7.8 Literature Survey: Integrating Explainable AI Techniques Into Natural Language Processing for Healthcare
7.9 Implementation Process
7.10 Results
7.11 Challenges and Future Directions
7.12 Future Directions
7.13 Ethical Considerations and Regulatory Implications
7.14 Conclusion
References
8. Introduction to Explainable AI in Healthcare: Enhancing Transparency and Trust
Karthik Srinivasan, Chaithanya Kumar Viralam Ramamurthy, Saravanan Matheswaran and Shermin Shamsudheen
8.1 Introduction to Explainable AI in Healthcare
8.1.1 The Evolution of AI in Healthcare
8.1.2 The Need for Explainable AI
8.2 Exploring Explainability Techniques
8.2.1 Machine Learning Explainability Methods
8.2.2 Explainability in Deep Learning
8.2.3 Explainable Natural Language Processing (NLP)
8.3 Real-World Applications and Case Studies
8.3.1 Enhancing Clinician Comprehension
8.3.2 Facilitating Regulatory Compliance
8.3.3 Fostering Patient Trust
8.3.4 Patient-Centric Applications of Explainable AI
8.3.4.1 Shared Decision Making
8.3.4.2 Personalized Care Planning
8.3.4.3 Empowering Patient Advocacy
8.4 Challenges and Ethical Considerations
8.4.1 Ethical Implications of Explainable AI
8.4.1.1 Discussion of Ethical Principles
8.4.1.2 Fairness
8.4.1.3 Accountability
8.4.1.4 Privacy
8.4.2 Balancing Transparency with Privacy
8.4.2.1 Pseudonymization and Data Anonymization
8.4.2.2 Granular Access Controls
8.4.2.3 Consent and Opt-Out Mechanisms
8.4.3 Interdisciplinary Collaboration and User-Centered Design
8.4.3.1 Importance of Collaboration
8.4.3.2 Ethical Review Boards
8.4.3.3 Ethics-Driven Design
8.4.4 User-Centered Design Principles
8.4.4.1 Human-Centered AI Interfaces
8.4.4.2 Incorporating User Feedback
8.4.4.3 Empowering User Agency
8.4.5 Regular Audits
8.4.6 Performance Assessments
8.4.7 Feedback Loops
8.5 Addressing Ethical Concerns and Algorithmic Biases
8.5.1 Ethical Decision-Support Tools: A Framework for Ethical Deliberation
8.5.1.1 Ethical Principles and Considerations
8.5.1.2 Patient Preferences and Values
8.5.1.3 Ethical Dilemma Analysis
8.5.1.4 Real-Time Guidance and Feedback
8.6 Future Directions and Considerations
8.6.1 Summary of Key Points
8.6.2 Importance of Explainable AI in Healthcare
8.6.3 Benefits of Transparency, Accountability, and Patient Trust
8.6.4 Future Directions in Explainable AI Research
8.6.5 Interpretability of Complex AI Models
8.6.6 Human-Centered Design of Explainable AI Systems
8.6.7 Ethical Considerations and Regulatory Compliance
8.7 Recommendations for Policymakers, Researchers, and Healthcare Practitioners
8.8 Conclusion
References
9. Interpretable Machine Learning Techniques
V. Kavitha, K. Suresh, G. Priyadharshini, Shaik Rasheeda Begum and R. Vidhya
9.1 Introduction
9.1.1 Definition of Interpretable Machine Learning
9.1.2 Importance of Interpretability in Machine Learning Models
9.1.3 Real-World Applications and Implications
9.2 Importance of Interpretability
9.2.1 Significance in Various Domains
9.2.2 Challenges Posed by Complex, Black-Box Models
9.2.3 Legal, Ethical, and Regulatory Considerations
9.3 Techniques for Interpretable Machine Learning
9.3.1 Intrinsic Methods
9.3.2 Post-Hoc Methods
9.3.3 Hybrid Approaches
9.3.3.1 Intrinsic Methods
9.3.3.2 Post-Hoc Methods
9.3.3.3 Hybrid Approaches
9.4 Intrinsic Methods
9.4.1 Decision Trees
9.4.1.1 How do Decision Trees Work?
9.4.1.2 Examples of Decision Tree Applications
9.4.2 Linear Models
9.4.2.1 When and How to Use Linear Models for Interpretability
9.4.2.2 Trade-Offs Between Interpretability and Model Performance
9.5 Post-Hoc Methods
9.5.1 Illustrative Examples
9.5.2 Partial Dependence Plots
9.5.2.1 Construction and Interpretation
9.5.2.2 Case Studies Demonstrating the Utility of Partial Dependence Plots
9.6 Hybrid Approaches
9.6.1 Local Interpretable Model-Agnostic Explanations (LIME)
9.6.1.1 Conceptual Overview
9.6.2 Implementation Details
9.6.3 Practical Examples
9.6.4 SHAP (SHapley Additive exPlanations)
9.6.4.1 How SHAP Values are Computed
9.6.4.2 Use Cases and Benefits
9.7 Evaluating Interpretability
9.7.1 Metrics for Evaluating Interpretability
9.7.2 Comparison of Different Techniques
9.7.3 Considerations for Choosing the Appropriate Method
9.8 Case Studies: Interpretable Machine Learning in Healthcare
9.8.1 Predicting Patient Readmission Risk
9.8.1.1 Challenges and Lessons Learned
9.8.2 Personalized Treatment Recommendations
9.8.2.1 Overview
9.8.2.2 Challenges and Lessons Learned
9.8.3 Early Detection of Disease Progression
9.8.3.1 Overview
9.8.3.2 Challenges and Lessons Learned
9.9 Risk Stratification for Preventive Care
9.9.1 Overview
9.9.2 Challenges and Lessons Learned
9.10 Conclusion
References
10. Interpretable Machine Learning Techniques in AI
Shavez, Poornima, Kanu Goyal, Shweta Sharma and Parul Sharma
10.1 Introduction
10.2 History
10.3 Making ML Interpretable
10.3.1 Benefits and Challenges to Interpretability
10.3.2 Challenges with Interpretable ML, Including
10.3.3 Limitations of Machine Learning
10.4 Machine Learning Models
10.4.1 Local Method Explanation
10.4.2 Global Method Explanation
10.5 Techniques of Interpretable Machine Learning
10.6 Model of Intrinsic Interpretable
10.6.1 Globally Interpretable Model
10.6.2 Adding Interpretability Constraints
10.6.3 Interpretable Model Extraction
10.6.4 Locally Interpretable Model
10.7 Post-Hoc Global Explanation
10.7.1 Traditional ML Explanation
10.7.2 Model Agnostic Explanation
10.7.3 Model-Specific Explanation
10.8 DNN Representation Explanation
10.8.1 Explanation of CNN Representation
10.8.2 Explanation of RNN Representation
10.9 Post-Hoc Local Explanation
10.9.1 Model-Agnostic Explanation
10.9.2 Explanation Based on Local Approximations
10.9.3 Perturbation-Based Explanation
10.9.4 Model-Specific Explanation
10.10 Backpropagation
10.11 Mask Perturbation
10.11.1 Investigation of Deep Representations
10.12 Conclusion
References
11. Interpretable Machine Learning Techniques in Medical System—The Role of Data Analytics and Machine Learning
Venkataraman P., Sunantha D. and Lakshmi S.
11.1 Introduction
11.1.1 Classification of Interpretable Methods
11.1.2 Disease Diagnosis Using Interpretable Machine Learning
11.1.3 Role of Interpretable Machine Learning
11.1.3.1 Importance of Interpretability in Healthcare Models
11.1.3.2 Challenges in Achieving Interpretability
11.1.3.3 Interpretability Techniques
11.1.3.4 Human–AI Collaboration
11.2 Materials and Methods
11.2.1 Diabetes Dataset
11.2.2 Machine Learning Algorithms
11.2.2.1 Support Vector Machine (SVM)
11.2.2.2 KNN
11.2.2.3 Random Forest
11.2.2.4 Decision Tree
11.2.2.5 Naïve Bayes
11.2.3 Diabetic Prediction Process
11.2.3.1 Data Preprocessing
11.2.3.2 Training and Testing
11.2.3.3 Implementation of the Classification Model
11.3 Experimental Result and Discussion
11.3.1 Performance Metrics
11.3.1.1 Accuracy
11.3.1.2 Confusion Matrix
11.3.1.3 F1-Score
11.3.1.4 Precision
11.3.1.5 Recall
11.3.2 Results and Discussion
11.4 Conclusion
References
12. Interpretable AI: Shedding Light on Medical Image Analysis Using Machine Learning Techniques
S. Bashyam, P. Supraja and Prithiviraj Rajalingam
12.1 Introduction
12.1.1 Interpretability in AI for Healthcare
12.1.2 Scope of the Chapter
12.2 Medical Image Analysis: A Critical Application of Machine Learning
12.2.1 Significance of Medical Image Analysis
12.2.2 Challenges and Complexities in Medical Image Data
12.2.3 Role of Machine Learning in Automating Image Analysis
12.3 Interpretable AI Techniques
12.3.1 Historical Roots of Deep Learning
12.3.2 Google Net: A Game-Changer
12.3.3 CNN Architecture
12.3.4 Types of Learning
12.3.4.1 Learning Problems
12.3.4.2 Hybrid Learning Problems
12.3.4.3 Statistical Inference
12.3.4.4 Learning Techniques
12.4 Accelerating Deep Learning Implementation with Open-Source Frameworks
12.4.1 Tensor Flow
12.4.2 Caffe
12.4.3 Caffe 2
12.4.4 ONNX (Open Neural Network Exchange)
12.4.5 Keras
12.4.6 Torch
12.4.7 Other Deep Learning Tools and Libraries
12.4.8 Additional Libraries and Frameworks
12.5 The Machine Learning Landscape in Medical Image Analysis
12.5.1 Deep Learning Networks: Tailored Architectures and Objectives
12.5.2 Image Registration: Aligning Medical Images for Enhanced Analysis
12.5.2.1 Objective and Techniques
12.5.2.2 Clinical Applications and Perspectives
12.5.2.3 Tools and Toolkits
12.5.3 Object Localization in Medical Image Analysis
12.5.3.1 Objective and Techniques
12.5.3.2 Challenges in Localization
12.5.3.3 Novel Strategies
12.5.4 Classification and Detection in Medical Image Analysis
12.5.4.1 Exam Classification
12.5.4.2 Object Classification
12.5.4.3 Classification Algorithms
12.5.4.4 Algorithm Types Related to Classification
12.5.4.5 Important Phrases Used in Classification Algorithms
12.5.4.6 Application in Disease Diagnosis
12.5.4.7 Transfer Learning for Disease Identification
12.5.4.8 Hierarchical Medical Image Classification
12.5.5 Object Detection in Medical Image Analysis
12.5.6 Segmentation
12.5.6.1 Substructure/Organ Segmentation
12.5.6.2 Lesion Segmentation
12.5.6.3 Role of Medical Image Segmentation
12.5.7 Impact of AI and Machine Learning
12.6 Use Cases and Applications: Interpretable AI’s Role in Brain Tumor Care—Diagnosis, Treatment, and Analysis
12.6.1 Precise Diagnosis: Unveiling Clarity
12.6.2 Customized Treatment Planning: Excellence in Every Detail
12.6.3 Radiomics Analysis: Expanding on It
12.6.4 Evaluation of Algorithm Performance
12.7 Challenges in Deep Learning for Medical Imaging
12.7.1 Imbalanced Data and Lack of Confidence Interval
12.7.2 Limited Availability of Annotated Data
12.7.3 Transition to End-to-End Learning
12.7.4 Data Augmentation Techniques
12.7.5 Interpretability and “Black-Box” Models
12.7.6 Resource-Intensive Training
References
13. Exploring the Role of Explainable AI in Women’s Health: Challenges and Solutions
Inam Ul Haq and Akib Mohi Ud Din Khanday
13.1 Introduction
13.2 The Importance of Addressing Women’s Health Concerns Beyond Major Diseases
13.3 The Role of Explainable AI (XAI) in Providing Precise Medicine Solutions Tailored to Women’s Specific Health Needs
13.4 Challenges in the Application of Explainable AI in Women’s Health
13.5 Women’s Health Concerns
13.6 The Promise of Machine Learning (ML) and Explainable AI (XAI) Technologies in Women’s Health
13.7 Potential Applications of Machine Learning (ML) and Explainable AI (XAI) Technologies in Women’s Health
13.8 Case Studies and Examples of Successful Implementations
13.9 Specific XAI Techniques and Methodologies
13.10 Conclusion
References
14. Explainable AI in Healthcare: Introduction
Amandeep Kaur and Sonali Goyal
14.1 Introduction to AI and Explainable AI
14.2 Introduction to Explainable AI in Healthcare
14.3 Applications of Explainable AI in Healthcare
14.4 Implementing Explainable AI in Healthcare: Practical Considerations
14.5 Future Directions and Emerging Trends
14.6 Conclusion
14.7 Future Work
References
15. Ethical Implications of Emotion Recognition Technology in Mental Healthcare: Navigating Privacy, Bias, and Therapeutic Boundaries
R. Ravi, V. Jeya Ramya, B. Prameela Rani, Srikanth Nalluri and M. Jenath
15.1 Introduction
15.2 Background
15.2.1 Objectives
15.3 Related Works
15.4 Literature Survey
15.4.1 Capabilities and Applications of ERT
15.4.2 Ethical Considerations in ERT
15.4.3 Clinical Applications and Efficacy of ERT
15.4.4 Challenges and Limitations of ERT
15.4.5 Future Directions and Recommendations
15.5 Methodology
15.5.1 Search Strategy for Literature Review
15.5.2 Selection Criteria for Inclusion of Studies
15.5.3 Data Extraction Process
15.5.4 Analysis of Literature
15.5.5 Validation and Peer Review
15.5.6 Ethical Considerations
15.6 Research Process: Emotion Recognition Technology (ERT) in Mental Healthcare
15.6.1 Defining the Research Scope and Objectives
15.6.2 Literature Review Protocol Development
15.6.3 Search Strategy Formulation
15.6.4 Literature Search and Screening
15.6.5 Selection of Studies
15.6.6 Data Extraction
15.6.7 Thematic Analysis
15.6.8 Synthesis and Interpretation
15.6.9 Validation and Peer Review
15.6.10 Ethical Considerations
15.7 Research Validation and Methodology Confirmation
15.7.1 Research Methodology
15.7.2 Research Execution
15.7.3 Validation Process
15.8 Results
15.8.1 Privacy Concerns
15.8.2 Algorithmic Biases
15.8.3 Accuracy and Validation
15.9 Future Directions
15.10 Conclusion
References
16. Bridging the Gap: Clinical Adoption and User Perspectives of Explainable AI in Healthcare
Shaik Masood Ahamed and J. Jabez
16.1 Introduction
16.2 Background
16.3 Research Objectives and Hypotheses
16.3.1 Research Objectives
16.3.2 Hypotheses
16.4 Related Works
16.5 Literature Survey
16.5.1 Technical Foundations of XAI
16.5.2 Impact on Clinical Decision Making
16.5.3 User Perspectives and Acceptance
16.5.4 Challenges and Considerations
16.5.5 Real-World Implementations and Case Studies
16.5.6 Interdisciplinary Perspectives
16.6 Proposed Methodology
16.6.1 Study Design
16.6.2 Data Collection Methods
16.6.3 Sampling Strategy
16.6.4 Data Analysis Ways
16.6.5 Ethical Considerations
16.7 Project Management
16.7.1 Surveys
16.7.2 Interviews
16.7.3 Observational Studies
16.7.4 Document Analysis
16.7.5 Focus Groups (if Applicable)
16.7.6 Data Analysis Plan
16.8 Quantitative Data Analysis
16.8.1 Descriptive Statistics
16.8.2 Inferential Statistics
16.8.3 Regression Analysis
16.8.4 Quantitative Integration
16.8.5 Qualitative Data Analysis
16.8.5.1 Data Coding
16.8.5.2 Theme Development
16.8.5.3 Interpretation and Conflation
16.9 Integration of Findings
16.9.1 Case Study: Implementation and Impact of Explainable AI (XAI) in Radiology
16.9.2 Key Research Questions to be Addressed in the Case Study May Include the Following
16.9.3 Literature Survey
16.9.3.1 Technical Foundations of XAI in Radiology
16.9.3.2 Clinical Applications and Impact of XAI in Radiology
16.9.3.3 User Perspectives and Acceptance of XAI in Radiology
16.9.3.4 Implementation Challenges and Considerations
16.9.3.5 Regulatory and Ethical Implications of XAI in Radiology
16.9.3.6 Real-World Implementations and Case Studies
16.9.3.7 Interdisciplinary Perspectives on XAI in Radiology
16.10 Methodology
16.10.1 Selection of Research Sites
16.10.2 Data Collection Instruments
16.11 Results
16.11.1 XAI Adoption and Awareness
16.11.2 Impact on Diagnostic Accuracy and Efficiency
16.11.3 User Perspectives and Experiences
16.11.4 Implementation Challenges and Considerations
16.11.5 Recommendations for Future Implementation
16.12 Conclusion
References
17. Application of AI-Based Technologies in the Healthcare Sector: Opportunities, Challenges, and Its Impact—Review
G. Jegadeeswari and B. Kirubadurai
17.1 Introduction
17.2 Literature Review, Methodology, and Analysis
17.3 Methodology
17.4 Analysis
17.5 Discussion and Limitations
Conclusion
References
18. A Complete Road Map for Interpretable Machine Learning Techniques Harnessing Various Real-Time Applications
A. Pandian, V. V. Ramalingam, J. Venkata Subramanian, K. Pradeep Mohan Kumar and S. Padmini
18.1 Introduction
18.2 Feature Importance
18.3 Rule-Based Models
18.4 Model Transparency
18.5 Visual Explanation
18.6 Interpretable Deep Learning
18.7 Evaluating Interpretability
18.8 Impact of the Current Study
18.9 Conclusion
References
19. Future Research Directions: Explainable Artificial Intelligence in Healthcare Industry
Shamneesh Sharma, Neha Kumra, Meghna Luthra, Vikas Verma and Komal Sharma
19.1 Introduction
19.2 Background and Literature Review
19.3 Explainable Artificial Intelligence Techniques in Healthcare
19.3.1 Local Interpretable Model-Agnostic Explanations
19.3.2 SHAP (SHapley Additive exPlanations)
19.3.3 Rule-Based XAI Techniques
19.4 Applications of XAI in Healthcare
19.5 Methodology and Result Analysis
19.5.1 Dataset Information
19.5.2 Research Methodology
19.5.3 Result Analysis
19.6 Future Directions
19.6.1 Research Area I || Interpretable Deep Learning Models
19.6.2 Research Area II || Dynamic and Adaptive Explanations
19.6.3 Research Area III || Integration With Electronic Health Records (EHRs)
19.6.4 Research Area IV || Human–AI Collaboration
19.6.5 Research Area V: Explainable Genomic Medicine
19.7 Conclusion
References
20. Real-World Applications of Explainable AI in Healthcare
Urvi, Parul Sharma, Kanu Goyal and Shweta Sharma
20.1 Introduction
20.1.1 Explainable Artificial Intelligence: Concepts and Techniques
20.1.2 Overview of Popular Explainable Artificial Intelligence Techniques
20.2 Real-World Applications of Explainable Artificial Intelligence in Healthcare
20.2.1 Diagnostic Imaging
20.2.2 Clinical Decision Support Systems (CDSS)
20.2.3 Drug Discovery and Development
20.2.4 Personalized Medicine
20.2.5 Patient Outcome Prediction
20.3 Challenges and Ethical Considerations for Explainable Artificial Intelligence Usage in Healthcare
20.4 Future Scope
20.5 Conclusion
References
21. Explainable AI in Medical Imaging, Personalized Medicine, and Bias Reduction: A New Era in Healthcare
Komal, Ganesh K. Sethi, Shamneesh Sharma and Rajender Kumar
21.1 Introduction
21.2 Explainable AI Techniques
21.3 Challenges in Healthcare AI
21.4 Complexity of Machine Learning Models
21.5 Explainable AI Techniques
21.6 Benefits of XAI in Healthcare
21.7 Improved Trust and Adoption by Healthcare Professionals
21.8 Case Studies of Explainable Artificial Intelligence
21.9 Challenges and Limitations
21.10 Future Directions
21.11 Conclusion
References
22. Understanding Explainability in Medical Imaging
Annie Silviya S. H., R. Tamizh Kuzhali, Akshaya V., Lakshmi Prabha T. S., Immanuvel Arokia James K. and B. Sriman
22.1 Introduction
22.1.1 Definition of Explainability in Medical Imaging
22.1.2 Importance of Explainability in Healthcare
22.1.3 Overview of the Chapter Structure
22.2 The Role of Medical Imaging in Healthcare
22.2.1 Brief History and Evolution of Medical Imaging Technologies
22.2.2 Current Significance of Medical Imaging in Healthcare
22.3 Explainability Challenges in Medical Imaging
22.3.1 Complexity of Medical Imaging Data and Techniques
22.3.2 Black-Box Nature of Deep Learning Models
22.3.3 Ethical and Regulatory Considerations in Medical AI
22.4 Techniques for Explainability in Medical Imaging
22.4.1 Feature Importance Methods
22.4.2 Saliency Maps and Gradient-Based Methods
22.4.3 Model-Agnostic Techniques
22.4.4 Rule-Based Systems
22.4.4.1 Case Study 1: Melanoma Classification with SHAP
22.4.4.2 Clinical Implications
22.4.4.3 Clinical Implications
22.5 Interpretability vs. Explainability
22.5.1 Importance of Providing Understandable Explanations
22.5.2 Advantages and Limitations of Explainability in Medical Imaging
22.5.3 Advantages of Explainable AI in Medical Imaging
22.5.3.1 Enhancing Trust and Transparency
22.5.3.2 Facilitating Collaboration Between AI Systems and Healthcare Professionals
22.5.4 Limitations and Challenges of Explainable AI in Medical Imaging
22.5.4.1 Trade-Offs Between Explainability and Performance
22.5.4.2 Interpreting Complex Models
22.5.4.3 Generalization and Robustness
22.6 Regulatory Landscape and Standards
22.6.1 Regulatory Frameworks in AI Healthcare
22.6.2 Standardization Efforts in AI Healthcare
22.6.3 Challenges and Future Directions
22.6.4 Future Directions
22.7 Case Studies and Applications of Explainable AI in Medical Imaging
22.7.1 Success Stories and Lessons Learned
Conclusion
References
23. Explainability and Regulatory Compliance in Healthcare: Bridging the Gap for Ethical XAI Implementation
Uma Maheswari Kalia Moorthy, Asthampatti Marimuthu Jayapalan Muthukumaran, Vijayalakshmi Kaliyaperumal, Shobana Jayakumar and Kalpana Ayanellore Vijayaraghavan
23.1 Introduction
23.2 Explainability Techniques and Methodologies
23.2.1 Real-World Examples of Explainable AI in Healthcare
23.3 Regulatory Landscape in Healthcare
23.3.1 Healthcare Regulations and Policies Related to AI Implementation
23.3.2 Data Privacy and Patient Confidentiality Considerations
23.3.3 Legal and Ethical Challenges in Deploying AI in Healthcare
23.3.3.1 Privacy and Security
23.3.3.2 Informed Consent
23.3.3.3 Bias and Fairness
23.3.3.4 Transparency and Explainability
23.3.3.5 Liability and Accountability
23.3.3.6 Clinical Validation and Regulation
23.3.3.7 Human Oversight and Autonomy
23.3.3.8 Consent for Research and Innovation
23.3.3.9 Equity and Access
23.4 Ensuring Regulatory Compliance
23.5 Strategies to Comply with Healthcare Regulations and Standards
23.6 The Role of Interdisciplinary Collaboration in Regulatory Compliance
23.7 Addressing Ethical Concerns and Mitigating Bias in AI Models
23.8 Trust and Transparency in Healthcare AI
23.8.1 Building Trust with Patients and Healthcare Professionals Through Explainable AI
23.8.2 The Impact of Transparency on AI Adoption and User Acceptance
23.8.3 Human–AI Interaction and the Role of Explanations in Decision Making
23.8.4 AI Projects in Healthcare That Lacked Transparency or Compliance
23.8.5 Ethical Considerations in Explainable AI Implementation
23.8.6 Ethical Principles for Explainable AI in Healthcare
23.8.7 Ensuring Patient Safety and Well-Being Through Explainable AI
23.8.8 Ethical Challenges in Balancing Privacy, Transparency, and Utility in Healthcare AI
23.9 Future Trends and Recommendations
References
24. Envisioning Explainable AI: Significance, Real-Time Applications, and Challenges in Healthcare
Kannan Chakrapani, Mohamed Iqubal Safa, Saranya Gangadhara Moorthy, Meenakshi Kumaraswamy and George Parimala
24.1 Introduction
24.2 Definition and Significance of XAI
24.3 Significance of Explainable AI (XAI)
24.4 Interpretable Decision Support Systems
24.5 Applications of XAI in Healthcare Industry
24.5.1 Application in Medical Diagnosis
24.6 Applications of XAI in Finance
24.7 Applications of XAI in Judiciary
24.8 Applications in the Judiciary
24.9 Challenges and Limitations of XAI
24.10 Limitations of XAI
24.11 Future Directions in XAI
Conclusion
References
25. Enlightened XAI: Illuminating Ethics and Equitable Explainability
Hemalatha P., Manikandan J., B. Balaji and V. Sujitha
25.1 Introduction
25.1.1 Definition of XAI
25.2 Ethical Considerations of AI in Healthcare
25.3 The Need for Ethical Guidelines in AI in Healthcare Development
25.4 Risks Associated with Opaque and Black-Box AI in Healthcare Models
25.5 Transparency and Interpretability in XAI
25.5.1 Importance of Transparency in Decision-Making Algorithms
25.5.2 Interpretable Models vs. Complex Models
25.5.3 Techniques for Achieving Interpretability in AI
25.6 Fairness in AI
25.6.1 Understanding Algorithmic Bias
25.7 Consequences of Biased AI Decision Making
25.8 Real-World Examples of AI Fairness Issues
25.9 Fairness in Explainable AI
25.10 The Tradeoff Between Fairness and Interpretability
25.11 Evaluating Fairness in Explainable AI Models
25.12 Guidelines and Frameworks for Ethical XAI
25.12.1 Existing Principles and Guidelines for Responsible XAI Development
25.12.2 Regulatory Considerations and Legal Frameworks
25.12.3 Industry Initiatives Promoting Ethical XAI
25.13 The Future of Ethical XAI
25.14 Conclusion
References
26. Enhancing Trust and Collaboration Using Explainability in Natural Language Processing for AI-Driven Healthcare
A. Pandian, K. Pradeep Mohankumar, S. Padmini, Sibi Amaran and K. Sreekumar
26.1 Introduction
26.1.1 Background of NLP in Healthcare
26.2 Importance of Explainability in NLP
26.3 Challenges of Black-Box NLP Models in Healthcare
26.4 Opacity of NLP Models
26.5 Addressing the Incompatibility of NLP Models
26.6 Implications of Lack of Explainability in Medical Decision Making
26.7 Necessity for Explainable AI in Healthcare
26.8 Building Trust and Confidence in NLP Predictions
26.9 Validation and Accountability in Medical Applications
26.10 Techniques for Achieving Explainability in NLP
26.10.1 Rule-Based Explanations
26.10.2 Visualizations
26.10.3 Grad-CAM (Gradient-weighted Class Activation Mapping)
26.10.4 Textual Explanation Generation
26.11 Advantages of Explainability for Healthcare Professionals
26.11.1 Empowering Healthcare Practitioners With AI Insights
26.11.2 Enhancing Collaboration between AI and Medical Experts
26.12 Future Directions and Challenges
26.13 Challenges Associated With Explainable AI for Healthcare
Conclusion
References
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