Explainable AI in Healthcare Imaging for Medical Diagnoses

Digital Revolution of Artificial Intelligence

1st Edition - March 29, 2025
Imprint: Academic Press
Editors: Tanzila Saba, Ahmad Taher Azar, Seifedine Kadry
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 3 9 7 9 - 3
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 3 9 7 8 - 6

In an era where Artificial Intelligence (AI) is revolutionizing healthcare, Explainable AI in Healthcare Imaging for Precision Medicine addresses the critical need for transpare… Read more

Explainable AI in Healthcare Imaging for Medical Diagnoses

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In an era where Artificial Intelligence (AI) is revolutionizing healthcare, Explainable AI in Healthcare Imaging for Precision Medicine addresses the critical need for transparency, trust, and accountability in AI-driven medical technologies. As AI becomes an integral part of clinical decision-making, especially in imaging and precision medicine, the question of how AI reaches its conclusions grows increasingly significant. This book explores how Explainable AI (XAI) is transforming healthcare by making AI systems more interpretable, reliable, and transparent, empowering clinicians and enhancing patient outcomes.

Through a comprehensive examination of the latest research, real-world case studies, and expert insights, this book delves into the application of XAI in medical imaging, disease diagnosis, treatment planning, and personalized care. It discusses the technical methodologies behind XAI, the challenges and opportunities of its integration into healthcare, and the ethical and regulatory considerations that will shape the future of AI-assisted medical decisions.

Key areas of focus include the role of XAI in improving diagnostic accuracy in fields such as radiology, pathology, and genomics and its potential to enhance collaboration between AI systems, healthcare professionals, and patients. The book also highlights practical applications of XAI in personalized medicine, showing how explainable models help tailor treatments to individual patients, and discusses how XAI can contribute to reducing bias and improving fairness in medical decision-making.

Written by leading experts in AI, healthcare, and precision medicine, Explain[S3G1] able AI in Healthcare Imaging for Precision Medicine is an essential resource for researchers, clinicians, students, and policymakers. Whether you are looking to stay at the forefront of AI innovations in healthcare or seeking to understand how explainability can build trust in AI systems, this book provides the insights and knowledge needed to navigate the evolving landscape of AI in medicine. It invites readers to explore how XAI can revolutionize healthcare and precision medicine, shaping a future where AI is both powerful and trustworthy.

Explainable AI in Healthcare Imaging for Medical Diagnoses
Cover image
Title page
Table of Contents
Copyright
Contributors
About the editors
Preface
1 Objectives of the book
2 Organization of the book
3 Features of the book
Acknowledgments
Chapter 1 Ensuring trust in healthcare robotics: The essential role of explainable AI
Abstract
1 Introduction
2 Challenges in AI-integrated healthcare robotics
2.1 Ethical concerns
2.2 Safety concerns
2.3 Complexity of AI systems
3 XAI solutions for trust healthcare robotics
3.1 Accuracy trade-off
3.2 Explanation depth
3.3 Contextual understanding
3.4 Post hoc explainability techniques
3.5 Evaluation metrics
3.6 The power of XAI
3.7 Examples of XAI benefits in healthcare robots
4 Emerging trends and the future of trust-based healthcare robotics
4.1 Advancing XAI hardware
4.2 Challenges in current methodologies
4.3 Future with trust-based healthcare robotics
5 Conclusion
References
Chapter 2 XAI implementation in traditional alternate medicine system
Abstract
1 Traditional medicine systems
1.1 Informatics in health care
1.2 Artificial intelligence
2 AI-based applications in the healthcare sectors
3 Explainable AI
3.1 Interpretability
3.2 Transparency
3.3 Explainability methods
3.4 XAI in healthcare industries
3.5 Transformation of health care industry for medical system
4 Traditional applications in XAI healthcare
4.1 XAI for drug discovery
4.2 XAI for clinical outputs sample trails
4.3 XAI clinical trials for traditional medical system
4.4 Clinical trial and module sharing
4.5 Patient care
5 Conclusion
References
Chapter 3 Explainable computational intelligence in bio and clinical medicine
Abstract
1 Introduction
1.1 Background and motivation
1.2 Objectives of computational intelligence in bio and clinical medicine
1.3 Scope and organization of the chapter
2 Fundamentals of computational intelligence
2.1 Machine learning algorithms
2.2 Artificial neural networks
2.3 Genetic algorithms
2.4 Data mining techniques
3 Data acquisition and preprocessing in bio and clinical medicine
3.1 Biological data sources
3.2 Clinical data collection and management
3.3 Data preprocessing techniques
4 Disease diagnosis and classification
4.1 Computational models for disease diagnosis
4.2 Image-based diagnosis
4.3 Biomarker discovery and validation
5 Treatment planning and personalized medicine
5.1 Treatment response prediction
5.2 Drug discovery and optimization
5.3 Patient-specific treatment plans
6 Healthcare management and decision support
6.1 Clinical decision support systems (CDSS)
6.2 Predictive analytics for hospital resource allocation
6.3 Telemedicine and remote patient monitoring
7 Ethical and regulatory considerations
7.1 Patient privacy and data security
7.2 Regulatory compliance in healthcare AI
7.3 Ethical implications of computational intelligence in medicine
8 Challenges and future directions
8.1 Interpretability and explainability of AI models
8.2 Integration with traditional medical practices
8.3 Incorporating real-time data streams
9 Case studies and applications
9.1 Case study 1: Early detection of cardiovascular diseases
9.2 Case study 2: Cancer diagnosis and treatment planning
9.3 Case study 3: Neurological disorders and computational neuromedicine
10 Conclusion
10.1 Key takeaways
10.2 Impact and future prospects
10.3 Call for collaboration between computational experts and medical professionals
References
Chapter 4 Enhancing medical AI interpretability using heatmap visualization techniques
Abstract
1 Introduction
2 Related work
2.1 Explainable artificial intelligence
2.2 Interpretability vs. explainability
2.3 Explaining medical images by visualization methods
3 Experiments
3.1 Experimental settings
3.2 Experimental results
4 Conclusion
References
Chapter 5 An interpretation-model-guided classification method for malignant pulmonary nodule
Abstract
1 Introduction
2 Related works
2.1 Machine-learning-based methods for classification of malignant and benign pulmonary nodules
2.2 Classification of pulmonary nodules based on interpretable methods
2.3 Shapley Additive exPlanations (SHAP) for machine learning interpretation
3 The proposed interpretation-model-guided classification method for malignant lung nodule
3.1 Semantic and radiomics features extraction using medical priori knowledge and image understanding
3.2 The proposed ISHAP-based model-guided algorithm for adaptive feature selection and classifier parameters setting
4 Experiments
4.1 Lung nodule dataset
4.2 Evaluation metrics
4.3 Experimental configuration
4.4 Implementation details and experimental results
5 Discussion
6 Conclusion
References
Chapter 6 Case studies: Explainable AI for Healthcare 5.0
Abstract
1 Introduction to Explainable AI
1.1 Overview of AI in healthcare
1.2 Importance of explainability
2 Case study 1: Interpretable models for diagnosing X
2.1 Problem statement and data
2.2 Model selection and training
2.3 Interpretability techniques applied
2.4 Results and clinical insights
3 Case study 2: Explainable AI in drug discovery
3.1 Drug discovery process overview
3.2 Application of Explainable AI
3.3 Case study results and insights
4 Ethical considerations in Explainable AI
4.1 Bias and fairness
4.2 Privacy concerns
4.3 Regulatory compliance
5 Case study 3: Predicting patient outcomes with interpretable models
5.1 Data collection and preprocessing
5.2 Model development and explainability
5.3 Clinical impact and feedback
6 Integration of Explainable AI into clinical practice
6.1 Challenges and solutions
6.2 Training and adoption by healthcare professionals
7 Future directions and advancements in Explainable AI for healthcare
7.1 Emerging technologies and trends
7.2 Potential impact on healthcare
8 Conclusion
References
Chapter 7 OML-GANs: An optimized multi-level generative adversarial networks model for multi-omics cancer subtype classification
Abstract
1 Introduction
2 Related works
3 The proposed methodology
3.1 OMICS dataset
3.2 The preprocessing using Standard Scaler
3.3 Feature extraction using K-means
3.4 Feature selection using SelectKBest
3.5 Data splitting
3.6 Classification based on multi-level GAN
3.7 Evaluation stage
4 Results and discussions
4.1 The results of preprocessing stage
4.2 The results of feature extraction stage
4.3 The results of feature selection stage
4.4 The results of classification stage based CNN with multi-level GAN
5 Conclusion
References
Chapter 8 Explainable artificial intelligence in epilepsy management: Unveiling the model interpretability
Abstract
Acknowledgment
1 Introduction
2 Exploring XAI in healthcare
2.1 Ethics of AI in healthcare
2.2 XAI diversity
2.3 XAI in EEG
2.4 The importance of XAI in healthcare
3 Architectural Framework for XAI
3.1 Varieties of XAI
3.2 XAI applications in healthcare
4 Understanding LIME
4.1 Key principles of LIME
4.2 Applications of LIME
5 Understanding SHAP
5.1 Key principles of SHAP
5.2 Applications of SHAP
6 A case study on seizures using EEG data
6.1 LIME analysis
6.2 SHAP analysis
6.3 Examples for LIME and SHAP
7 Key insights from the study
8 Wrapping up and looking ahead
9 Challenges and opportunities
References
Chapter 9 Revolutionizing cancer diagnosis with AI-enhanced histopathology and deep learning: A study on enhanced image analysis and model explainability
Abstract
1 Introduction
1.1 Contributions
1.2 Organization
2 Related works
3 Proposed methodology
3.1 Dataset
3.2 Data preprocessing
3.3 Model architecture
3.4 Explainable artificial intelligence (AI) approaches
4 Result & discussions
4.1 Experimental setup
4.2 Evaluation matrix
4.3 Result analysis
5 Conclusion
6 Conclusion and future direction
6.1 Conclusion
6.2 Future directions
References
Chapter 10 Unveiling explainable artificial intelligence (XAI) in advancing precision medicine: An overview
Abstract
1 Introduction
2 Research questions
3 Research objectives
4 The role of explainable AI (XAI) in demystifying AI models: Building trust in precision medicine
5 Improving diagnostic accuracy in precision medicine through explainable artificial intelligence (XAI)
6 Explainable artificial intelligence (XAI) tailoring personalized treatment plans in precision medicine
7 Conclusion
References
Chapter 11 Pneumonia and brain tumors diagnosis using machine learning algorithms
Abstract
1 Introduction
1.1 Problem statement
1.2 Aims & objectives
1.3 Scope and limits
1.4 Ethical considerations
2 Literature review
2.1 Convolutional neural networks
2.2 Review of specific diseases
3 Methodology/implementation
3.1 Datasets
3.2 Pre-processing
4 Evaluation
5 Results and discussion
5.1 Pneumonia
5.2 Brain Tumors
5.3 Radiology image type
6 Graphical user interface
7 Conclusion
7.1 Future work
References
Chapter 12 Explainable artificial intelligence in medical research: A synopsis for clinical practitioners—Comprehensive XAI methodologies
Abstract
1 Introduction
2 Clinical decision support systems with XAI
2.1 Explainability of intricate decision procedures
2.2 Patient safety and responsiveness
2.3 Improving model performance
3 Several biomarker types used in medicine using XAI
3.1 Cancer biomarkers
3.2 XAI's function in neurological disease biomarker analysis
4 XAI methods
4.1 SHAP (SHapley Additive exPlanations)
4.2 LIME (Local Interpretable Model-agnostic Explanations)
4.3 CEM (Contrastive Explanation Method)
4.4 DeepLIFT (Deep Learning Important Features)
4.5 Integrated Gradients
4.6 Grad-CAM (Gradient-weighted Class Activation Mapping)
4.7 Permutation Feature Importance
4.8 Counterfactual explanations
5 Conclusion
References
Chapter 13 Advancing explainable AI and deep learning in medical imaging for precision medicine and ethical healthcare
Abstract
Acknowledgment
1 Introduction
1.1 The need for Explainable Artificial Intelligence in the medical field
1.2 Deficiencies in existing approaches
1.3 Contributions
1.4 Chapter organization
2 Literature review
2.1 History of Explainable Artificial Intelligence
2.2 Background knowledge
2.3 State-of-the-art schemes
3 Methodology
3.1 Overview of medical imaging and XAI integration
3.2 Diverse medical imaging modalities
3.3 Image acquisition and pre-processing
3.4 Medical image analysis (image registration and object detection)
3.5 Substructure segmentation
3.6 Feature explanation methods
3.7 Classification method
3.8 Proposed model
3.9 Evaluation metrics for deep learning models
4 Result analysis and discussion
4.1 Dataset
4.2 Performance analysis
4.3 Ablation experiments
4.4 Challenges and future directions
5 Summary/conclusion
References
Chapter 14 Leveraging explainable AI in deep learning for brain tumor detection
Abstract
Availability of data and materials
Conflicts of interest
1 Introduction
2 Deep learning in brain tumor recognition
2.1 Interpretability in brain tumor detection
3 Deep learning models for Brain tumor recognition
3.1 Convolution neural networks
3.2 Recurrent neural network (RNN)
3.3 Attention model
3.4 Autoencoder
3.5 Generative adversarial networks
4 Transfer learning approach
4.1 Choosing a pre-trained model
4.2 Modifying the architecture
4.3 Fine-tuning the model
4.4 Loss function
4.5 Evaluation metrics
4.6 Post-processing
5 Multimodal approaches
6 Importance of interpretability in tumor detection using deep learning models
6.1 Importance of interpretability in clinical settings
6.2 Challenge of black box models in tumor detection
6.3 Impact on trust and adoption in healthcare
6.4 Legal and ethical implications
6.5 Tools for improving interpretability
6.6 Balancing accuracy and interpretability
6.7 Role of explainability in improving model performance
6.8 Explainability in tumor sub-region detection
6.9 Addressing uncertainty with explainability
6.10 Future directions and research opportunities
7 Conclusion
References
Chapter 15 Unveiling the root causes of diabetes using explainable AI
Abstract
Acknowledgments
1 Introduction
2 Explainable artificial intelligence
2.1 Explainable AI in healthcare
2.2 Approaches of XAI
2.3 Local interpretable model-agnostic explanations (LIME)
2.4 Importance of interpretability in AI models
3 Understanding diabetes prediction models
3.1 Challenges with black box models
3.2 Interpreting and understanding diabetes prediction models
3.3 Machine learning algorithms
4 Comprehending LIME and SHAP methods
5 Conclusion and future directions
References
Chapter 16 Explainable AI for melanoma diagnosis through dermosopic images: Recent findings and future directions
Abstract
Conflict of interest
1 Introduction
1.1 Image analysis techniques of skin lesion
1.2 Challenges in skin lesion detection
1.3 Publically available datasets
1.4 Performance metrics
2 Literature review
2.1 Melanoma detection through conventional machine learning
2.2 Melanoma detection through deep learning techniques
3 Discussion
4 Conclusion
References
Chapter 17 Enhancing multi-omics cancer subtype classification using explainable convolutional neural networks
Abstract
1 Introduction
2 Related work
3 The proposed CNN Omics approach
3.1 Omics dataset
3.2 The preprocessing using Standard Scaler
3.3 Omics feature extraction using K-Means
3.4 Feature selection using SelectKBest
3.5 Classification based on Explainable CNN
4 Results and discussions
4.1 Discussion
5 Conclusion
References
Chapter 18 Explainable convolutional neural network for Parkinson's disease detection
Abstract
Conflict of interest
1 Introduction
2 Related works
3 The proposed methodology
3.1 PD dataset
3.2 Preprocessing stage based on GANs
3.3 PD Feature Extraction phase using principal component analysis (PCA)
3.4 Feature Selection phase using the Select KBest algorithm
3.5 Dataset Splitting phase
3.6 The CNN model for PD classification
3.7 Evaluation phase
4 CNN results of classification stage
4.1 Classification CNN results on dataset A
4.2 Classification CNN results on dataset B
4.3 Classification CNN results on dataset C
5 Conclusion
References
Chapter 19 Data analytics and cognitive computing for digital health: A generic approach and a review of emerging technologies, challenges, and research directions
Abstract
1 Introduction
2 Related work
3 A generic approach using DL and NLP for continuous remote monitoring in digital health
3.1 Deep learning module
3.2 Natural language processing (NLP) module
4 Emerging technologies in data analytics and cognitive computing in healthcare
5 Real world case studies of data analytics and cognitive computing in healthcare
5.1 Disease diagnosis
5.2 Treatment planning
5.3 Patient monitoring
6 Challenges and opportunities of implementing data analytics and cognitive computing in healthcare
6.1 Challenges
6.2 Opportunities
7 Future research directions in data analytics and cognitive computing in healthcare
8 Conclusion and future scope
References
Chapter 20 New challenges and opportunities to explainable artificial intelligence (XAI) in smart healthcare
Abstract
1 Introduction
2 The role of XAI in smart healthcare
3 Emerging challenges of XAI in smart healthcare
3.1 Data heterogeneity
3.2 Explainability of different AI models
3.3 User-centered explanations
3.4 Interpreting multiple causes
3.5 Integrating XAI into existing healthcare systems
4 Opportunities unveiled by XAI in smart healthcare
4.1 Precision diagnostics
4.2 Fostering patient engagement and informed consent
4.3 Monitoring for bias in AI models
4.4 Identifying bias in training data
4.5 Fostering inclusive healthcare environments
5 Case studies of XAI in smart healthcare
5.1 Skin lesion analysis
5.2 Heart failure risk prediction
5.3 Drug discovery
6 Future directions and research agenda
6.1 Novel explainability methods
6.2 Elevating explanatory techniques
6.3 Seamless integration into clinical workflows
6.4 Cocreation of human-AI synergy
7 Conclusion
References
Index

Tanzila Saba

Prof. Tanzila Saba is a Research Professor and Associate Chair of the Information Systems Department in the College of Computer and Information Sciences, Prince Sultan University, Riyadh, KSA. Her primary research focus in recent years is medical imaging, pattern recognition, data mining, MRI analysis, and soft computing. She led more than 15 research-funded projects. She has full command of various subjects and taught several courses at the graduate and postgraduate levels. She is Senior Member of IEEE. Dr. Tanzila is Leader of Artificial Intelligence & Data Analytics Research Lab at PSU and Active Professional Member of ACM, AIS, and IAENG organizations. She is PSU WiDS (Women in Data Science) Ambassador at Stanford University.

Affiliations and expertise

Research Professor and Associate Chair, Information Systems Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh, KSA

Ahmad Taher Azar

Ahmad Azar is a Research Associate Professor at the Prince Sultan University, Riyadh, Kingdom Saudi Arabia. He is also an associate professor at the Faculty of Computers and Artificial intelligence, in Benha University, Egypt. He is the Editor in Chief of the International Journal of System Dynamics Applications (IJSDA), International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), and International Journal of Intelligent Engineering Informatics (IJIEI), among others. He is currently Associate Editor of ISA Transactions, Elsevier, and the IEEE systems journal. Dr. Azar works in the areas of control theory & applications, process control, chaos control and synchronization, nonlinear control, renewable energy, computational intelligence.

Affiliations and expertise

Research Associate Professor, Prince Sultan University, Riyadh, Kingdom Saudi ArabiaAssociate Professor, Faculty of Computers and Artificial intelligence, Benha University, Egypt

Seifedine Kadry

Seifedine Kadry is a Professor in the Department of Mathematics and Computer Science, at Norrof University College, in Norway. He has a Bachelor’s degree in 1999 from Lebanese University, MS degree in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in 2017 from Rouen University. At present, his research focuses on data Science, education using technology, system prognostics, stochastic systems, and applied mathematics. He is an ABET program evaluator for computing, and ABET program evaluator for Engineering Tech. He is a Fellow of IET, Fellow of IETE, and Fellow of IACSIT. He is a distinguished speaker of IEEE Computer Society.

Affiliations and expertise

Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon; Department of Applied Data Science, Noroff University College, Kristiansand, Norway

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Explainable AI in Healthcare Imaging for Medical Diagnoses

Digital Revolution of Artificial Intelligence

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Tanzila Saba

Ahmad Taher Azar

Seifedine Kadry

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