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Submit a ProposalGenerative Artificial Intelligence in Healthcare
One Line Description
Unlock the future of modern medicine with this essential, end-to-end guide on leveraging generative AI, deep learning, and predictive modeling to streamline clinical workflows, accelerate drug discovery, and dramatically improve patient outcomes.
Audience
Healthcare professionals, medical researchers, clinical and data scientists, and AI developers seeking to understand how AI technologies can enhance diagnostics, treatment, and patient care.
Healthcare professionals, medical researchers, clinical and data scientists, and AI developers seeking to understand how AI technologies can enhance diagnostics, treatment, and patient care.
Description
The intersection of artificial intelligence and healthcare is one of the most rapidly evolving areas of technological advancement today. With the advent of generative AI, large language models, and advanced deep learning techniques, the medical field is experiencing a shift from traditional practices to data-driven, intelligent solutions. These technologies are now capable of assisting in disease prediction, automating diagnostics, supporting clinical decision-making, and even generating synthetic data for rare medical conditions, making healthcare more precise, efficient, and accessible. This book provides a timely and comprehensive exploration of how artificial intelligence is reshaping the medical landscape. From predictive motion modeling in ICUs to AI-powered drug discovery and personalized treatment plans, this book brings together the latest advancements, practical applications, and real-world case studies that demonstrate the potential of AI to improve patient outcomes and revolutionize clinical workflows. Covering a wide range of topics, the book also addresses critical concerns around data privacy, regulatory compliance, and the responsible deployment of AI in clinical environments. Designed for both academic and professional audiences, it serves as a foundational resource for healthcare practitioners, researchers, developers, and decision-makers aiming to harness AI’s power for building smarter, more sustainable, and patient-centric healthcare systems.
Readers will find the volume:
• Explores the future of medicine through real-world applications of generative AI, deep learning, and predictive modeling in healthcare;
• Presents practical insights with case studies on AI-driven diagnostics, treatment planning, and personalized medicine;
• Examines ethical and regulatory aspects of integrating AI in clinical settings, with a focus on privacy, transparency, and patient safety;
• Highlights AI’s role in healthcare innovation, from infrastructure improvements to advanced drug development processes.
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The intersection of artificial intelligence and healthcare is one of the most rapidly evolving areas of technological advancement today. With the advent of generative AI, large language models, and advanced deep learning techniques, the medical field is experiencing a shift from traditional practices to data-driven, intelligent solutions. These technologies are now capable of assisting in disease prediction, automating diagnostics, supporting clinical decision-making, and even generating synthetic data for rare medical conditions, making healthcare more precise, efficient, and accessible. This book provides a timely and comprehensive exploration of how artificial intelligence is reshaping the medical landscape. From predictive motion modeling in ICUs to AI-powered drug discovery and personalized treatment plans, this book brings together the latest advancements, practical applications, and real-world case studies that demonstrate the potential of AI to improve patient outcomes and revolutionize clinical workflows. Covering a wide range of topics, the book also addresses critical concerns around data privacy, regulatory compliance, and the responsible deployment of AI in clinical environments. Designed for both academic and professional audiences, it serves as a foundational resource for healthcare practitioners, researchers, developers, and decision-makers aiming to harness AI’s power for building smarter, more sustainable, and patient-centric healthcare systems.
Readers will find the volume:
• Explores the future of medicine through real-world applications of generative AI, deep learning, and predictive modeling in healthcare;
• Presents practical insights with case studies on AI-driven diagnostics, treatment planning, and personalized medicine;
• Examines ethical and regulatory aspects of integrating AI in clinical settings, with a focus on privacy, transparency, and patient safety;
• Highlights AI’s role in healthcare innovation, from infrastructure improvements to advanced drug development processes.
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Author / Editor Details
M. Manikandan, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering, Presidency University, Bengaluru, Karnataka, India. He has published more than 30 research publications and 16 patents and has invented three products. His research interests include nanoelectronics, GaN technology, device and circuit simulation, nanoscale device design, and modeling.
M. Manikandan, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering, Presidency University, Bengaluru, Karnataka, India. He has published more than 30 research publications and 16 patents and has invented three products. His research interests include nanoelectronics, GaN technology, device and circuit simulation, nanoscale device design, and modeling.
Malathy Sathyamoorthy, PhD is an Assistant Professor in the Department of Information Technology at KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. She has published more than 20 research papers in international journals, 15 papers in international conferences, two patents, and four book chapters. Her research interests include wireless sensor networks, networking, security and machine learning.
Rajesh Kumar Dhanaraj, PhD is a Professor at Symbiosis International University, Pune, India. He has authored and edited more than 50 books on various cutting-edge technologies, holds 21 patents, and has contributed more than 100 articles and papers to refereed journals and international conferences. His research interests encompass machine learning, cyber-physical systems, and wireless sensor networks.
Sumithra M. G. PhD is a Principal and Professor in the Department of Electronics and Communication Engineering at Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India. She has published more than 80 research papers in various journals, 98 papers in international conferences, 12 patents, and five book chapters.
Rutvij H. Jhaveri, PhD is an experienced educator and researcher working in the Department of Computer Science and Engineering at Pandit Deendayal Energy University, Gandhinagar, India. He has authored more than 145 articles in international journals and conferences and several national and international patents and copyrights. His research interests include cybersecurity, IoT systems, and smart healthcare.
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Table of Contents
Preface
1. Transforming Healthcare with Artificial Intelligence and Machine Learning
Charan Muthukumaran, J. Jayashree, J. Vijayashree and Haleema P.K.
1.1 Artificial Intelligence (AI)
1.1.1 Definition of AI
1.1.2 History of Artificial Intelligence
1.1.3 Types of Artificial Intelligence
1.1.4 Features of AI
1.2 Machine Learning (ML)
1.2.1 Definition of ML
1.2.2 Types of Machine Learning
1.2.3 Features of Machine Learning
1.2.4 Broad Applications of Machine Learning
1.3 Healthcare and Modern Healthcare
1.3.1 Evolution of Healthcare: From Traditional to Modern
Healthcare
1.3.2 Key Components of Modern Healthcare
1.3.3 Importance of Technology in Modern Healthcare
1.4 Intro to AI and ML in Healthcare
1.4.1 Role of AI and ML in Modern Healthcare
1.4.2 Challenges of Using AI and ML in Modern Healthcare
1.4.3 Real-World Applications of AI in Modern Healthcare
1.5 Impact of AI and ML in Healthcare
1.5.1 AI in Disease Diagnosis
1.5.1.1 AI in Diagnosis of Parkinson’s Disease
1.5.1.2 AI in Diagnosis of Alzheimer’s Disease
1.5.1.3 AI in Diagnosis of Coronary Heart Disease
1.5.1.4 AI in Diagnosis of Prostate Cancer
1.5.2 AI and ML in Patient Care and Monitoring
1.5.2.1 AI and ML in Preventing Bedsores for Patients
1.5.3 AI and ML in Medical Imaging
1.5.3.1 AI in Interventional Radiology
1.5.4 Predictive Analysis in Healthcare
1.5.5 AI and ML in Drug Discovery and Genomics
1.5.5.1 Lead Optimization in Drug Discovery
1.5.5.2 AI in Precision Medicine
1.5.6 AI in Modern Healthcare Issues
1.5.6.1 AI in Coronavirus Management
1.5.6.2 AI in Prediction of General Anxiety Disorder (GAD)
1.5.7 Ethical Considerations in AI and ML in Healthcare
1.5.8 Future Trends of AI and ML in Healthcare
1.6 Conclusion
References
2. Integration of Artificial Intelligence, Healthcare, and Information Technology: Challenges, Issues and Solutions
Indhumathi G., Saranya G., A. S. Augustine Fletcher, Naveen Kumar R., Ajayan J. and Sreejith S.
2.1 Introduction
2.2 Integration with Information Technology
2.2.1 Interoperability Challenges
2.2.2 Cloud-Based AI Solutions
2.3 Ethical and Regulatory Considerations
2.3.1 Case Study: Ethical Dilemmas in AI Diagnostics
2.3.2 Transparency and Explainable AI
2.4 Technological Solutions and Future Directions
2.5 Challenges and Issues in Integrating AI with Healthcare IT
2.5.1 Technical Challenges
2.5.2 Organizational Challenges
2.5.3 Ethical and Legal Challenges
2.6 Solutions and Strategies
2.6.1 Technological Solutions
2.6.2 Organizational Strategies
2.6.3 Regulatory Reforms
2.6.4 Future Directions
2.7 Case Studies: Successful Integration Examples
2.7.1 Case Study 1: Implementation of AI in Radiology Departments
2.7.1.1 Implementation Details
2.7.1.2 Benefits
2.7.2 Case Study 2: Use of AI-Driven Predictive Analytics in Patient Admissions
2.7.2.1 Implementation Details
2.7.2.2 Benefits
2.7.2.3 Challenges in Achieving Interoperability and Data Integration
2.7.3 Case Study 3: AI in Personalized Medicine
2.7.3.1 Implementation Details
2.7.3.2 Benefits
2.7.3.3 Challenges
2.7.4 Case Study 4: AI in Remote Patient Monitoring
2.7.4.1 Implementation Details
2.7.4.2 Benefits
2.7.4.3 Challenges
2.8 Comparative Analysis: Global AI Adoption in Healthcare
2.9 Trends and Innovations in AI Healthcare
2.9.1 AI Enabled Telemedicine
2.9.2 AI Chatbots for Primary Healthcare Assistance
2.9.3 Blockchain Integration with AI in Healthcare
2.9.4 AI in Medical Imaging
2.9.5 Ethical AI Deployment Framework
2.9.5.1 Data Privacy and Security
2.9.5.2 Bias Reduction
2.9.5.3 Transparent and Explainable AI
2.9.5.4 Human Monitoring
2.9.5.5 Safety and Reliability
2.9.5.6 Overview of Regulatory Frameworks
2.9.5.7 Ethical Principles for AI in Healthcare
2.9.5.8 Steps for Ethical AI Integration
2.10 Conclusion
References
3. Revolutionizing Healthcare: The Impact of Generative AI
and Large Language Models on Medical Science
Sureswar Reddy Dronadula, Shlok Chetan Kulkarni, Chirag Jitendra Chandnani, Haleema, Vedik Agarwal, Vijayashree J. and Jayashree J.
3.1 Chapter Introduction
3.2 The History of Generative AI and LLMs
3.2.1 Shift from Rule-Based Systems to Machine Learning
3.2.2 Rise of Neural Networks and Deep Learning
3.2.3 Advancement of Generative Models
3.2.4 Emergence of Large Language Models (LLMs)
3.2.5 Utilization of Artificial Intelligence in the Field of Medicine
3.3 Introduction to Generative AI and Large Language Models
3.3.1 LLMs
3.4 The Evolution of AI in Healthcare
3.4.1 Early Applications: Rule-Based Systems
3.4.2 Machine Learning and Data-Driven Insights
3.4.3 Introduction of Deep Learning
3.4.4 Application of NLP in the Healthcare Industry
3.4.5 Incorporation of Artificial Intelligence into Clinical Workflows
3.5 Application of Generative AI in Healthcare
3.6 AI-Powered Drug Discovery
3.7 Enhancing Patient Care with AI
3.8 AI in Medical Imaging and Radiology
3.9 Predictive Analytics in the Healthcare Industry
3.9.1 Introduction
3.9.2 The Application of Generative AI in Predictive Analytics for Healthcare
3.9.3 Utilizing LLMs for Predictive Analytics in Healthcare
3.9.4 Challenges and Ethical Constraints
3.10 Ethical Considerations and Challenges of Generative AI in Healthcare
3.11 Regulatory Landscape for Generative AI in Healthcare
3.12 The Future of Telemedicine and AI
3.12.1 The Role and Benefits of AI in Telemedicine
3.13 The Role of AI in Mental Health Diagnostics
3.13.1 Treatment Plans Based on Artificial Intelligence
3.13.2 Enhancing Accessibility and Reducing Stigma
3.14 AI in Healthcare Administration and Operations
3.14.1 Enhancing Efficiency through Automation
3.14.2 Optimizing Resource Allocation
3.14.3 Personalized Patient Care
3.15 The Future of Generative AI in Healthcare
3.15.1 Transforming Diagnostics and Treatment
3.15.2 Personalized Medicine and Predictive Analytics
3.15.3 Ethical Implication and the Future Expectations
Conclusion
Bibliography
4. Artificial Intelligence–Based Predictive Motion Modeling
in ICU
Manikanda Prabu N., Rajesh K. P., Vinu Kumar S. M. and Rajasekar P.
4.1 Introduction
4.1.1 Brief Overview of Challenges in ICU Surveillance Applications
4.1.2 The Importance of AI in Improving Surveillance and Patient Monitoring
4.1.3 Introduction to Predictive Motion Modeling
4.2 Background and Related Work
4.2.1 Existing Methods for Object Detection and Tracking
4.2.2 Limitations of Current Technologies in ICU Settings
4.3 Neuro-Fuzzy–Based Human Motion Detection System with Hybrid Feature Set
4.3.1 Feature Extraction Using Hybrid Feature Set
4.3.1.1 Histogram-Oriented Gradient (HOG) Feature
4.3.1.2 Local Binary Pattern Feature
4.3.1.3 Hybrid Feature Set
4.3.2 Feature Normalization
4.4 Classification Using ANFIS Classifier
4.4.1 Fuzzy Logic Classifier
4.4.2 Artificial Neural Network Classifier
4.4.3 Adaptive Neuro-Fuzzy Inference System (ANFIS) Classifier
4.5 Results and Discussion
4.5.1 Comparison with Conventional Methods
4.5.2 Time Complexity Analysis
4.5.3 Robustness to Lighting Conditions
4.5.4 Handling Occlusions
4.6 Conclusion
References
5. Deep Learning Approaches for Detection of Osteoporosis
Using Texture Analysis of Calcaneal Radiographs
Aditya Sriram and Anu Shaju Areeckal
5.1 Introduction
5.2 Background Theory
5.2.1 Preprocessing Techniques
5.2.1.1 Dynamic Range Manipulation
5.2.1.2 Histogram Equalization
5.2.1.3 Adaptive Equalization
5.2.2 Deep Learning and Neural Networks
5.2.2.1 Evaluation Metrics for Neural Networks
5.2.3 Texture Characterization
5.2.3.1 Histogram Bins
5.2.3.2 Local Binary Pattern (LBP)
5.2.3.3 Gabor Filter
5.3 Proposed Methodology
5.3.1 Dynamic Range Correction
5.3.2 Histogram Equalization
5.3.3 Augmentation of Data
5.3.4 Deep Learning Model
5.3.5 Parameter Testing and Trial Data
5.3.6 L-Layer Network
5.3.7 Result Analysis from the Neural Network
5.3.8 LBP Model Comparison
5.3.9 LBP Histogram Comparison
5.3.10 Gabor Features
5.3.11 Gabor Features Via CNN
5.3.12 GAN Model
5.3.13 Software Tool
5.4 Result Analysis
5.4.1 Image Data Augmentation
5.4.2 Deep Learning Model
5.4.3 GAN Model
5.4.4 Deep Learning Model on Actual Dataset
5.4.5 LBP (Local Binary Pattern) and Corresponding Results
5.4.6 Gabor Filter Outputs and Result Analysis
5.5 Conclusion and Future Scope
5.5.1 Work Conclusion
5.5.2 Challenges Faced
5.5.3 Future Scope
References
6. Recent Development and Challenges in Ocular Disease Detection: An Artificial Intelligence–Based Perspective
Kirti Singh, Deepika Raina and Prachi Mall
6.1 Introduction
6.2 Development of AI in Ophthalmology
6.2.1 Diabetic Retinopathy
6.2.2 Glaucoma
6.2.3 Age-Related Macular Degeneration
6.2.4 Cataract and Other Ophthalmologic Diseases
6.3 Challenges in the Application of AI
6.3.1 Strengthening the Data Foundation
6.3.2 Reporting Guidelines
6.3.3 Security
6.3.4 Ethics
6.4 Future Directions
6.5 Conclusions
References
7. Designing Algorithms for Generative Artificial Intelligence–Based Disease Prediction and Analysis in Healthcare 4.0
Nayarah Shabir, Shabia Shabir, Seema Sharma, Sheema Khan and Parveen Kumar Lehana
7.1 Introduction
7.2 Healthcare 4.0
7.3 Healthcare 4.0 Framework
7.4 Speech-Based Analysis in Healthcare
7.5 Face-Based Analysis in Healthcare
7.6 Test Report-Based Analysis in Healthcare
7.7 Future Trends in Healthcare
Bibliography
8. Security, Privacy, and Information-Sharing Considerations of Artificial Intelligence (AI) in Healthcare
Nipun Sharma and Swati Sharma
8.1 Introduction
8.2 Security
8.3 Privacy
8.4 Information-Sharing
8.5 Ethical Considerations
8.6 Challenges
8.7 Future Directions
References
9. Ethical Policies, Standards, and Practical Issues in Adopting Generative AI for Digital Health Transformation
Shanthini J., Sumithra M. G., Dhivyasri G., M. Manikandan,
Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Rutvij H. Jhaveri
9.1 Introduction
9.1.1 Digital Health Transformation: An Overview
9.1.2 The Impact of Generative AI on the Transformation of Healthcare
9.2 Ethical Considerations in Adopting Generative AI
9.2.1 Privacy and Data Protection
9.2.2 Current Healthcare Regulations and Evolving Norms
9.2.3 Challenges and Risks
9.2.4 Informed Consent
9.2.5 Transparency and Explainability
9.2.6 Bias and Fairness
9.2.7 Accountability and Responsibility
9.3 Regulatory Landscape of Generative AI in Digital Health
Transformation
9.3.1 Current HC Regulations (e.g., HIPAA and GDPR)
9.3.1.1 HIPAA
9.3.1.2 General Data Protection Regulation (GDPR)
9.3.2 Emerging Standards for AI in Healthcare
9.3.3 International Policy Frameworks
9.3.4 Certification and Accreditation Processes
9.4 Challenges and Risks of Generative AI in Digital Health
Transformation
9.4.1 Data Security and Cyber Threats
9.4.2 Misuse and Malpractice Concerns
9.4.3 Impact on Healthcare Workforce
9.4.4 Patient Trust and Adoption Barriers
9.5 Implementing GenAI for Digital Health Transformation
9.5.1 Integrating Generative AI into Clinical Workflows
9.5.2 Training and Educating Healthcare Professionals
9.5.3 Continuous Monitoring and Quality Assurance
9.5.4 Collaborative Approaches with Stakeholders
9.6 Adopting Generative AI for Digital Health Transformation
9.6.1 Diagnostic and Predictive Analytics
9.6.2 Personalized Treatment Plans
9.6.3 Remote Patient Monitoring and Telemedicine
9.6.4 AI-Enhanced Medical Research
9.7 Technological and Practical Implications of Adopting Generative AI for Digital Health Transformation
9.7.1 Data Quality and Availability
9.7.2 Interoperability Challenges
9.7.3 Scalability and Maintenance of AI Systems
9.7.4 Cost-Benefit Analysis for Health Institutions
9.8 The Future Perspectives
9.8.1 Potential Advancements in Generative AI for Healthcare
9.8.2 Addressing Long-Term Ethical and Societal Impacts
9.8.3 Recommendations for Policymakers and Practitioners
9.9 Conclusion
9.9.1 Summary of Ethical Imperatives and Strategic Recommendations
9.9.2 Final Thoughts on Sustainable Digital Health Transformation
References
10. Case Study on Generative Artificial Intelligence (GAI)
in Health-Based Risk Prediction: Developing a Framework for Future Research
Bhupinder Singh, Christian Kaunert and Saurabh Chandra
10.1 Introduction
10.1.1 Background and Significance of Study
10.1.2 Objectives of the Chapter
10.1.3 Structure of the Chapter
10.2 Quantum Computing in Healthcare
10.3 Applications of Quantum Computing in Healthcare
10.4 Generative Adversarial Networks (GANs) in Healthcare
10.5 Applications of GANs in Healthcare
10.6 Assimilation of Quantum Computing and GANs in Healthcare
10.7 Conclusion and Future Scope
Bibliography
11. A Case Study on the Use of Adversarial Learning and GANs in Clinical Practice to Predict Biomarkers and Age-Associated Macular Dystrophy in Elderly
S.B. Abitha, K. Shivashanker, S. Mangai, S. Anbumani, Tharun V. and M. Manikandan
11.1 Introduction
11.1.1 Eye Anatomy
11.2 Age-Related Macular Dystrophy
11.3 Normal and Diabetic Retinopathy
11.3.1 Diabetes Impact on Eyes
11.4 Age-Related Macular Degeneration Types
11.4.1 Normal
11.4.2 Drusen
11.4.3 Diabetic Macular Edema (DME)
11.4.4 Choroidal Neovascularization (CNV)
11.5 Optical Coherence Tomography (OCT)
11.6 Convolutional Neural Network
11.7 Biomarkers of Susceptibility
11.7.1 Age Prevalence of AMD
11.7.2 Sex
11.7.3 Pigment of Iris
11.7.4 BMI
11.7.5 High Blood Pressure
11.8 Adversarial Learning
11.8.1 Generative Adversarial Network (GAN)
11.8.1.1 GAN Architecture
11.8.1.2 Generator
11.8.1.3 Discriminator
11.8.1.4 Pretraining
11.9 GAN in Clinical Practice
11.9.1 GANs Working
11.10 Dataset
11.10.1 Data Splitting and Augmentation
11.10.2 Data Preprocessing
11.10.3 BM3D Filter
11.10.4 CLAHE Technique
11.11 Process of GAN
11.11.1 Working Process
11.12 Conclusion
11.12.1 The Pivotal Role of GANs in AMD Prediction
11.12.2 Addressing Challenges in Clinical Implementation
11.12.3 Future Directions
Bibliography
12. Case Study on Generative Artificial Intelligence (GAI)–Based Drug Development Process in Healthcare Industries
Nipun Sharma and Swati Sharma
12.1 Introduction
12.2 Accelerated Drug Discovery
12.3 Improved Target Identification
12.4 Optimized Clinical Trials
12.5 Integration Validation and Explainability
12.6 Ethical and Regulatory Considerations
12.7 Open Innovation, Future Direction, and Challenges
12.8 Conclusion
References
13. Revolutionizing Patient Care: Generative AI’s Role in Personalized Medicine
Sunil Sharma, Megha Sen, Lokesh Tharani, Sandip Das and Bhupendra Soni
13.1 Introduction
13.1.1 Overview of Personalized Medicine
13.1.2 The Emergence of Generative AI in Healthcare
13.1.3 Objective of the Chapter
13.2 The Foundation of Generative AI in Healthcare
13.2.1 Core Principles and Technologies
13.2.2 Historical Development
13.2.3 Enabling Factors
13.2.4 Impact on Healthcare
13.2.5 Key Technologies Enabling Generative AI
13.3 Generative AI Applications in Personalized Medicine
13.3.1 Personalized Treatment Plans
13.3.2 Predictive Analytics for Patient Outcomes
13.3.3 Custom Drug Design and Development
13.3.4 Medical Imaging and Diagnostics
13.3.5 Synthetic Data Generation for Study and Protection of Privacy
13.3.6 Regulation and Ethical Issues
13.3.7 Management of Chronic Diseases
13.3.7.1 Customized Treatment Plans
13.3.7.2 Forecasted Health Results
13.3.7.3 Improved Tracking and Distant Medical Attention
13.3.7.4 Customization and Substance Creation
13.3.7.5 Involvement of the Patient and Self-Management
13.3.7.6 Data Privacy and Ethical Considerations
13.4 Analyzing Data for Personalization
13.4.1 Genomic Data Analysis
13.4.2 Electronic Health Records (EHRs)
13.4.3 Real-Time Health Monitoring
13.4.4 Privacy-Preserving Methods
13.4.5 Personalized Medicine with Electronic Health Records (EHRs)
13.4.5.1 Methodologies for Utilizing EHRs in Personalized Medicine
13.4.5.2 EHR Use for Personalized Medicine
13.4.5.3 Issues to be Thought About
13.4.6 Data Integration and Interoperability Challenges
13.4.6.1 Technical Difficulties
13.4.6.2 Privacy and Ethical Considerations
13.4.6.3 Concerns about Standards and Regulation
13.5 Ethical, Legal, and Social Implications
13.5.1 Ethical Considerations
13.5.2 Legal Implications
13.5.3 Social Implications
13.5.4 Regulatory Frameworks and Compliance
13.6 Prospects for Generative AI and Customized Healthcare
13.7 Scalability and Adoption in Clinical Settings
13.7.1 Techniques for Effective Scalability and Acceptance
13.8 The Role of Interdisciplinary Collaboration
13.8.1 Encouraging All-Inclusive Resolutions
13.8.2 Improving Innovation by Utilizing Various Viewpoints
13.8.3 Surmounting Data Challenges
13.8.4 Encouraging the Acceptance and Application
13.8.5 Increasing the Use of Patient-Centered Care
13.9 Conclusion
Bibliography
14. Transforming Healthcare Infrastructure through Generative Artificial Intelligence: A Focus on Sustainability, Efficiency, and Patient Centricity
Sana Shabir Khan, Seema Sharma, Sheema Khan and Asif Hussain Shah
14.1 Introduction
14.1.1 IoT in Healthcare
14.2 AI in Healthcare Infrastructure
14.3 Introduction to Generative AI in Healthcare
14.4 Applications in Medical Diagnostics
14.4.1 Drug Discovery and Development
14.4.2 Virtual Health Assistants
14.4.3 Medical Research
14.5 Enhancing Efficiency and Patient Centricity
14.5.1 Transforming Healthcare Infrastructure
14.5.2 Case Study: AI-Driven Hospital Design
14.5.3 Case Study: Smart Clinics
14.5.4 Case Study: Predictive Analytics in Patient Care
14.5.5 Optimizing Drug Discovery Processes
14.5.6 Patient-Centric Virtual Health Assistants
14.6 Ensuring Sustainability in Healthcare Delivery
14.7 Ethical and Regulatory Challenges
14.8 Future Trends and Innovations
14.8.1 Case Studies and Success Stories
14.9 Infrastructure Design and AI Integration
14.9.1 Smart Cities and Healthcare Integration
14.9.1.1 Resilience and Disaster Preparedness
14.9.1.2 Data Analytics and Decision Support Systems
14.9.1.3 Regenerative and Sustainable Infrastructure
14.9.1.4 Ethical and Social Implications
14.9.1.5 Collaboration with Healthcare Professionals
14.10 Conclusion: Transforming Healthcare through Generative AI
Bibliography
15. Recent Deep Learning Techniques for Medical Image Diagnostics: Case Study on Segmentation of Exudates in Fundus Images
Jethe Krushi, Aritro Santra and Anu Shaju Areeckal
15.1 Fundamental Concepts
15.1.1 Linear Algebra
15.1.2 Conditional Probability
15.2 Data Preparation
15.2.1 Augmentation
15.2.2 Train-Validation-Test Sets
15.3 Deep Learning with Images
15.3.1 Classification Tasks
15.3.2 Segmentation Tasks
15.3.3 Generation Tasks
15.4 Deep Learning Architectures
15.4.1 Neural Networks
15.4.2 Convolutional Neural Networks (CNNs)
15.4.3 Vision Transformers
15.4.4 Generative Models
15.4.4.1 Generative Adversarial Networks (GANs)
15.4.4.2 Diffusion Models
15.4.5 Multi-Modal Architectures
15.5 Evaluation Metrics for Generative Tasks
15.5.1 Inception Score (IS)
15.5.2 Fréchet Inception Distance (FID)
15.5.3 Kernel Inception Distance (KID)
15.5.4 Precision
15.5.5 Recall
15.6 Case Study on Diabetic Retinopathy: Segmentation, Grading, and Data Augmentation Techniques
15.6.1 Segmentation of Exudates Using ResNet50 and SqueezeNet
15.6.2 Architectures from Literature for Enhancing Segmentation
15.6.3 Data Augmentation Using GANs
15.6.4 Using GANs for Segmentation and Classification
15.6.4.1 Repurposing GANs for Classification
15.6.4.2 Repurposing GANs for Segmentation
15.7 Conclusion
References
16. Deep Learning for EEG-Based Epileptic Seizure Detection: A Review
Mukesh Pandey, Latika Narayan, Pratibha Pandey, Upinder Kaur and Salil Bharany
16.1 Introduction
16.2 Literature Review
16.3 Methods for Epileptic Seizure Detection
16.3.1 Minimum Variance
16.3.2 Amplitude Thresholding
16.3.3 Lossless EEG Data Compression
16.3.4 Temporal Synchronization
16.3.5 Minimizing the False Alarm Rate
16.3.6 Common Spatial Pattern
16.4 Role of DL Techniques in ESD
16.4.1 Multi Perception–Based Seizure Detection
16.4.2 Focus Localization–Based Detection
16.4.3 Handcrafted-Based Detection
16.4.4 1D-CNN Technique–Based Detection
16.5 Challenges in Epileptic Seizure Detection
16.6 Conclusion and Future Direction
References
17. Smart Cane Development for the Visually Impaired:
Fusion of Sensor and Computer Vision Technologies
Chalachew Dagnew Yehuala, Rajan John, Nagarajan. S. and Umamaheswaran. S.
17.1 Introduction
17.1.1 Obstacles and Prospects for the Future
17.1.2 Knowledge Gap
17.1.3 Importance and Impact
17.1.4 Objectives
17.2 Related Work
17.3 Methodology
17.3.1 Operational Principles
17.4 Components
17.4.1 Object Detection and Recognition
17.4.1.1 Computer Vision–Based Solutions
17.4.1.2 Sensor-Based Solutions
17.4.2 Camera and Raspberry Pi Interfacing
17.4.2.1 Estimation of the Field of View (FOV)
17.4.2.2 Configuring the Camera with the Raspberry Pi
17.4.3 Ultrasonic Sensor Configuration
17.4.3.1 Sensor Placement and Configuration
17.4.3.2 Voltage Divider Circuit
17.4.4 Water Detection
17.4.4.1 Interfacing with Raspberry Pi
17.4.4.2 System Programming for Water Detection
17.4.5 Location Tracking
17.4.5.1 GPS Module Usage
17.4.5.2 Interfacing GSM/SIM 900A with Raspberry Pi
17.4.6 Power Source
17.4.6.1 Pi Sugar 2 Pro
17.4.7 Prototype Manufacturing
17.5 Empirical Preparations
17.5.1 Dataset and Augmentation
17.5.2 Training
17.6 Empirical Analysis and Evaluations
17.6.1 Implementation
17.6.2 Performance Discussion
17.7 Conclusion and Future Work
References
18. Generative Adversarial Networks in COVID-19 Image
Augmentation and Its Future Prospects: A Systematic Review
Radhika J.M., J. Ajayan, Sandip Bhattacharya and Sreejith. S.
18.1 Introduction
18.2 Different Types of Generative Adversarial Networks
18.3 Generative Adversarial Networks in COVID-19 Image Augmentation and Segmentation
18.4 Recent Application of Generative Adversarial Networks in Various Other Lung Image Augmentation and Segmentation Methods
18.5 Future Prospects of Generative Adversarial Networks
18.6 Conclusion
References
Index
Back to Top
Preface
1. Transforming Healthcare with Artificial Intelligence and Machine Learning
Charan Muthukumaran, J. Jayashree, J. Vijayashree and Haleema P.K.
1.1 Artificial Intelligence (AI)
1.1.1 Definition of AI
1.1.2 History of Artificial Intelligence
1.1.3 Types of Artificial Intelligence
1.1.4 Features of AI
1.2 Machine Learning (ML)
1.2.1 Definition of ML
1.2.2 Types of Machine Learning
1.2.3 Features of Machine Learning
1.2.4 Broad Applications of Machine Learning
1.3 Healthcare and Modern Healthcare
1.3.1 Evolution of Healthcare: From Traditional to Modern
Healthcare
1.3.2 Key Components of Modern Healthcare
1.3.3 Importance of Technology in Modern Healthcare
1.4 Intro to AI and ML in Healthcare
1.4.1 Role of AI and ML in Modern Healthcare
1.4.2 Challenges of Using AI and ML in Modern Healthcare
1.4.3 Real-World Applications of AI in Modern Healthcare
1.5 Impact of AI and ML in Healthcare
1.5.1 AI in Disease Diagnosis
1.5.1.1 AI in Diagnosis of Parkinson’s Disease
1.5.1.2 AI in Diagnosis of Alzheimer’s Disease
1.5.1.3 AI in Diagnosis of Coronary Heart Disease
1.5.1.4 AI in Diagnosis of Prostate Cancer
1.5.2 AI and ML in Patient Care and Monitoring
1.5.2.1 AI and ML in Preventing Bedsores for Patients
1.5.3 AI and ML in Medical Imaging
1.5.3.1 AI in Interventional Radiology
1.5.4 Predictive Analysis in Healthcare
1.5.5 AI and ML in Drug Discovery and Genomics
1.5.5.1 Lead Optimization in Drug Discovery
1.5.5.2 AI in Precision Medicine
1.5.6 AI in Modern Healthcare Issues
1.5.6.1 AI in Coronavirus Management
1.5.6.2 AI in Prediction of General Anxiety Disorder (GAD)
1.5.7 Ethical Considerations in AI and ML in Healthcare
1.5.8 Future Trends of AI and ML in Healthcare
1.6 Conclusion
References
2. Integration of Artificial Intelligence, Healthcare, and Information Technology: Challenges, Issues and Solutions
Indhumathi G., Saranya G., A. S. Augustine Fletcher, Naveen Kumar R., Ajayan J. and Sreejith S.
2.1 Introduction
2.2 Integration with Information Technology
2.2.1 Interoperability Challenges
2.2.2 Cloud-Based AI Solutions
2.3 Ethical and Regulatory Considerations
2.3.1 Case Study: Ethical Dilemmas in AI Diagnostics
2.3.2 Transparency and Explainable AI
2.4 Technological Solutions and Future Directions
2.5 Challenges and Issues in Integrating AI with Healthcare IT
2.5.1 Technical Challenges
2.5.2 Organizational Challenges
2.5.3 Ethical and Legal Challenges
2.6 Solutions and Strategies
2.6.1 Technological Solutions
2.6.2 Organizational Strategies
2.6.3 Regulatory Reforms
2.6.4 Future Directions
2.7 Case Studies: Successful Integration Examples
2.7.1 Case Study 1: Implementation of AI in Radiology Departments
2.7.1.1 Implementation Details
2.7.1.2 Benefits
2.7.2 Case Study 2: Use of AI-Driven Predictive Analytics in Patient Admissions
2.7.2.1 Implementation Details
2.7.2.2 Benefits
2.7.2.3 Challenges in Achieving Interoperability and Data Integration
2.7.3 Case Study 3: AI in Personalized Medicine
2.7.3.1 Implementation Details
2.7.3.2 Benefits
2.7.3.3 Challenges
2.7.4 Case Study 4: AI in Remote Patient Monitoring
2.7.4.1 Implementation Details
2.7.4.2 Benefits
2.7.4.3 Challenges
2.8 Comparative Analysis: Global AI Adoption in Healthcare
2.9 Trends and Innovations in AI Healthcare
2.9.1 AI Enabled Telemedicine
2.9.2 AI Chatbots for Primary Healthcare Assistance
2.9.3 Blockchain Integration with AI in Healthcare
2.9.4 AI in Medical Imaging
2.9.5 Ethical AI Deployment Framework
2.9.5.1 Data Privacy and Security
2.9.5.2 Bias Reduction
2.9.5.3 Transparent and Explainable AI
2.9.5.4 Human Monitoring
2.9.5.5 Safety and Reliability
2.9.5.6 Overview of Regulatory Frameworks
2.9.5.7 Ethical Principles for AI in Healthcare
2.9.5.8 Steps for Ethical AI Integration
2.10 Conclusion
References
3. Revolutionizing Healthcare: The Impact of Generative AI
and Large Language Models on Medical Science
Sureswar Reddy Dronadula, Shlok Chetan Kulkarni, Chirag Jitendra Chandnani, Haleema, Vedik Agarwal, Vijayashree J. and Jayashree J.
3.1 Chapter Introduction
3.2 The History of Generative AI and LLMs
3.2.1 Shift from Rule-Based Systems to Machine Learning
3.2.2 Rise of Neural Networks and Deep Learning
3.2.3 Advancement of Generative Models
3.2.4 Emergence of Large Language Models (LLMs)
3.2.5 Utilization of Artificial Intelligence in the Field of Medicine
3.3 Introduction to Generative AI and Large Language Models
3.3.1 LLMs
3.4 The Evolution of AI in Healthcare
3.4.1 Early Applications: Rule-Based Systems
3.4.2 Machine Learning and Data-Driven Insights
3.4.3 Introduction of Deep Learning
3.4.4 Application of NLP in the Healthcare Industry
3.4.5 Incorporation of Artificial Intelligence into Clinical Workflows
3.5 Application of Generative AI in Healthcare
3.6 AI-Powered Drug Discovery
3.7 Enhancing Patient Care with AI
3.8 AI in Medical Imaging and Radiology
3.9 Predictive Analytics in the Healthcare Industry
3.9.1 Introduction
3.9.2 The Application of Generative AI in Predictive Analytics for Healthcare
3.9.3 Utilizing LLMs for Predictive Analytics in Healthcare
3.9.4 Challenges and Ethical Constraints
3.10 Ethical Considerations and Challenges of Generative AI in Healthcare
3.11 Regulatory Landscape for Generative AI in Healthcare
3.12 The Future of Telemedicine and AI
3.12.1 The Role and Benefits of AI in Telemedicine
3.13 The Role of AI in Mental Health Diagnostics
3.13.1 Treatment Plans Based on Artificial Intelligence
3.13.2 Enhancing Accessibility and Reducing Stigma
3.14 AI in Healthcare Administration and Operations
3.14.1 Enhancing Efficiency through Automation
3.14.2 Optimizing Resource Allocation
3.14.3 Personalized Patient Care
3.15 The Future of Generative AI in Healthcare
3.15.1 Transforming Diagnostics and Treatment
3.15.2 Personalized Medicine and Predictive Analytics
3.15.3 Ethical Implication and the Future Expectations
Conclusion
Bibliography
4. Artificial Intelligence–Based Predictive Motion Modeling
in ICU
Manikanda Prabu N., Rajesh K. P., Vinu Kumar S. M. and Rajasekar P.
4.1 Introduction
4.1.1 Brief Overview of Challenges in ICU Surveillance Applications
4.1.2 The Importance of AI in Improving Surveillance and Patient Monitoring
4.1.3 Introduction to Predictive Motion Modeling
4.2 Background and Related Work
4.2.1 Existing Methods for Object Detection and Tracking
4.2.2 Limitations of Current Technologies in ICU Settings
4.3 Neuro-Fuzzy–Based Human Motion Detection System with Hybrid Feature Set
4.3.1 Feature Extraction Using Hybrid Feature Set
4.3.1.1 Histogram-Oriented Gradient (HOG) Feature
4.3.1.2 Local Binary Pattern Feature
4.3.1.3 Hybrid Feature Set
4.3.2 Feature Normalization
4.4 Classification Using ANFIS Classifier
4.4.1 Fuzzy Logic Classifier
4.4.2 Artificial Neural Network Classifier
4.4.3 Adaptive Neuro-Fuzzy Inference System (ANFIS) Classifier
4.5 Results and Discussion
4.5.1 Comparison with Conventional Methods
4.5.2 Time Complexity Analysis
4.5.3 Robustness to Lighting Conditions
4.5.4 Handling Occlusions
4.6 Conclusion
References
5. Deep Learning Approaches for Detection of Osteoporosis
Using Texture Analysis of Calcaneal Radiographs
Aditya Sriram and Anu Shaju Areeckal
5.1 Introduction
5.2 Background Theory
5.2.1 Preprocessing Techniques
5.2.1.1 Dynamic Range Manipulation
5.2.1.2 Histogram Equalization
5.2.1.3 Adaptive Equalization
5.2.2 Deep Learning and Neural Networks
5.2.2.1 Evaluation Metrics for Neural Networks
5.2.3 Texture Characterization
5.2.3.1 Histogram Bins
5.2.3.2 Local Binary Pattern (LBP)
5.2.3.3 Gabor Filter
5.3 Proposed Methodology
5.3.1 Dynamic Range Correction
5.3.2 Histogram Equalization
5.3.3 Augmentation of Data
5.3.4 Deep Learning Model
5.3.5 Parameter Testing and Trial Data
5.3.6 L-Layer Network
5.3.7 Result Analysis from the Neural Network
5.3.8 LBP Model Comparison
5.3.9 LBP Histogram Comparison
5.3.10 Gabor Features
5.3.11 Gabor Features Via CNN
5.3.12 GAN Model
5.3.13 Software Tool
5.4 Result Analysis
5.4.1 Image Data Augmentation
5.4.2 Deep Learning Model
5.4.3 GAN Model
5.4.4 Deep Learning Model on Actual Dataset
5.4.5 LBP (Local Binary Pattern) and Corresponding Results
5.4.6 Gabor Filter Outputs and Result Analysis
5.5 Conclusion and Future Scope
5.5.1 Work Conclusion
5.5.2 Challenges Faced
5.5.3 Future Scope
References
6. Recent Development and Challenges in Ocular Disease Detection: An Artificial Intelligence–Based Perspective
Kirti Singh, Deepika Raina and Prachi Mall
6.1 Introduction
6.2 Development of AI in Ophthalmology
6.2.1 Diabetic Retinopathy
6.2.2 Glaucoma
6.2.3 Age-Related Macular Degeneration
6.2.4 Cataract and Other Ophthalmologic Diseases
6.3 Challenges in the Application of AI
6.3.1 Strengthening the Data Foundation
6.3.2 Reporting Guidelines
6.3.3 Security
6.3.4 Ethics
6.4 Future Directions
6.5 Conclusions
References
7. Designing Algorithms for Generative Artificial Intelligence–Based Disease Prediction and Analysis in Healthcare 4.0
Nayarah Shabir, Shabia Shabir, Seema Sharma, Sheema Khan and Parveen Kumar Lehana
7.1 Introduction
7.2 Healthcare 4.0
7.3 Healthcare 4.0 Framework
7.4 Speech-Based Analysis in Healthcare
7.5 Face-Based Analysis in Healthcare
7.6 Test Report-Based Analysis in Healthcare
7.7 Future Trends in Healthcare
Bibliography
8. Security, Privacy, and Information-Sharing Considerations of Artificial Intelligence (AI) in Healthcare
Nipun Sharma and Swati Sharma
8.1 Introduction
8.2 Security
8.3 Privacy
8.4 Information-Sharing
8.5 Ethical Considerations
8.6 Challenges
8.7 Future Directions
References
9. Ethical Policies, Standards, and Practical Issues in Adopting Generative AI for Digital Health Transformation
Shanthini J., Sumithra M. G., Dhivyasri G., M. Manikandan,
Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Rutvij H. Jhaveri
9.1 Introduction
9.1.1 Digital Health Transformation: An Overview
9.1.2 The Impact of Generative AI on the Transformation of Healthcare
9.2 Ethical Considerations in Adopting Generative AI
9.2.1 Privacy and Data Protection
9.2.2 Current Healthcare Regulations and Evolving Norms
9.2.3 Challenges and Risks
9.2.4 Informed Consent
9.2.5 Transparency and Explainability
9.2.6 Bias and Fairness
9.2.7 Accountability and Responsibility
9.3 Regulatory Landscape of Generative AI in Digital Health
Transformation
9.3.1 Current HC Regulations (e.g., HIPAA and GDPR)
9.3.1.1 HIPAA
9.3.1.2 General Data Protection Regulation (GDPR)
9.3.2 Emerging Standards for AI in Healthcare
9.3.3 International Policy Frameworks
9.3.4 Certification and Accreditation Processes
9.4 Challenges and Risks of Generative AI in Digital Health
Transformation
9.4.1 Data Security and Cyber Threats
9.4.2 Misuse and Malpractice Concerns
9.4.3 Impact on Healthcare Workforce
9.4.4 Patient Trust and Adoption Barriers
9.5 Implementing GenAI for Digital Health Transformation
9.5.1 Integrating Generative AI into Clinical Workflows
9.5.2 Training and Educating Healthcare Professionals
9.5.3 Continuous Monitoring and Quality Assurance
9.5.4 Collaborative Approaches with Stakeholders
9.6 Adopting Generative AI for Digital Health Transformation
9.6.1 Diagnostic and Predictive Analytics
9.6.2 Personalized Treatment Plans
9.6.3 Remote Patient Monitoring and Telemedicine
9.6.4 AI-Enhanced Medical Research
9.7 Technological and Practical Implications of Adopting Generative AI for Digital Health Transformation
9.7.1 Data Quality and Availability
9.7.2 Interoperability Challenges
9.7.3 Scalability and Maintenance of AI Systems
9.7.4 Cost-Benefit Analysis for Health Institutions
9.8 The Future Perspectives
9.8.1 Potential Advancements in Generative AI for Healthcare
9.8.2 Addressing Long-Term Ethical and Societal Impacts
9.8.3 Recommendations for Policymakers and Practitioners
9.9 Conclusion
9.9.1 Summary of Ethical Imperatives and Strategic Recommendations
9.9.2 Final Thoughts on Sustainable Digital Health Transformation
References
10. Case Study on Generative Artificial Intelligence (GAI)
in Health-Based Risk Prediction: Developing a Framework for Future Research
Bhupinder Singh, Christian Kaunert and Saurabh Chandra
10.1 Introduction
10.1.1 Background and Significance of Study
10.1.2 Objectives of the Chapter
10.1.3 Structure of the Chapter
10.2 Quantum Computing in Healthcare
10.3 Applications of Quantum Computing in Healthcare
10.4 Generative Adversarial Networks (GANs) in Healthcare
10.5 Applications of GANs in Healthcare
10.6 Assimilation of Quantum Computing and GANs in Healthcare
10.7 Conclusion and Future Scope
Bibliography
11. A Case Study on the Use of Adversarial Learning and GANs in Clinical Practice to Predict Biomarkers and Age-Associated Macular Dystrophy in Elderly
S.B. Abitha, K. Shivashanker, S. Mangai, S. Anbumani, Tharun V. and M. Manikandan
11.1 Introduction
11.1.1 Eye Anatomy
11.2 Age-Related Macular Dystrophy
11.3 Normal and Diabetic Retinopathy
11.3.1 Diabetes Impact on Eyes
11.4 Age-Related Macular Degeneration Types
11.4.1 Normal
11.4.2 Drusen
11.4.3 Diabetic Macular Edema (DME)
11.4.4 Choroidal Neovascularization (CNV)
11.5 Optical Coherence Tomography (OCT)
11.6 Convolutional Neural Network
11.7 Biomarkers of Susceptibility
11.7.1 Age Prevalence of AMD
11.7.2 Sex
11.7.3 Pigment of Iris
11.7.4 BMI
11.7.5 High Blood Pressure
11.8 Adversarial Learning
11.8.1 Generative Adversarial Network (GAN)
11.8.1.1 GAN Architecture
11.8.1.2 Generator
11.8.1.3 Discriminator
11.8.1.4 Pretraining
11.9 GAN in Clinical Practice
11.9.1 GANs Working
11.10 Dataset
11.10.1 Data Splitting and Augmentation
11.10.2 Data Preprocessing
11.10.3 BM3D Filter
11.10.4 CLAHE Technique
11.11 Process of GAN
11.11.1 Working Process
11.12 Conclusion
11.12.1 The Pivotal Role of GANs in AMD Prediction
11.12.2 Addressing Challenges in Clinical Implementation
11.12.3 Future Directions
Bibliography
12. Case Study on Generative Artificial Intelligence (GAI)–Based Drug Development Process in Healthcare Industries
Nipun Sharma and Swati Sharma
12.1 Introduction
12.2 Accelerated Drug Discovery
12.3 Improved Target Identification
12.4 Optimized Clinical Trials
12.5 Integration Validation and Explainability
12.6 Ethical and Regulatory Considerations
12.7 Open Innovation, Future Direction, and Challenges
12.8 Conclusion
References
13. Revolutionizing Patient Care: Generative AI’s Role in Personalized Medicine
Sunil Sharma, Megha Sen, Lokesh Tharani, Sandip Das and Bhupendra Soni
13.1 Introduction
13.1.1 Overview of Personalized Medicine
13.1.2 The Emergence of Generative AI in Healthcare
13.1.3 Objective of the Chapter
13.2 The Foundation of Generative AI in Healthcare
13.2.1 Core Principles and Technologies
13.2.2 Historical Development
13.2.3 Enabling Factors
13.2.4 Impact on Healthcare
13.2.5 Key Technologies Enabling Generative AI
13.3 Generative AI Applications in Personalized Medicine
13.3.1 Personalized Treatment Plans
13.3.2 Predictive Analytics for Patient Outcomes
13.3.3 Custom Drug Design and Development
13.3.4 Medical Imaging and Diagnostics
13.3.5 Synthetic Data Generation for Study and Protection of Privacy
13.3.6 Regulation and Ethical Issues
13.3.7 Management of Chronic Diseases
13.3.7.1 Customized Treatment Plans
13.3.7.2 Forecasted Health Results
13.3.7.3 Improved Tracking and Distant Medical Attention
13.3.7.4 Customization and Substance Creation
13.3.7.5 Involvement of the Patient and Self-Management
13.3.7.6 Data Privacy and Ethical Considerations
13.4 Analyzing Data for Personalization
13.4.1 Genomic Data Analysis
13.4.2 Electronic Health Records (EHRs)
13.4.3 Real-Time Health Monitoring
13.4.4 Privacy-Preserving Methods
13.4.5 Personalized Medicine with Electronic Health Records (EHRs)
13.4.5.1 Methodologies for Utilizing EHRs in Personalized Medicine
13.4.5.2 EHR Use for Personalized Medicine
13.4.5.3 Issues to be Thought About
13.4.6 Data Integration and Interoperability Challenges
13.4.6.1 Technical Difficulties
13.4.6.2 Privacy and Ethical Considerations
13.4.6.3 Concerns about Standards and Regulation
13.5 Ethical, Legal, and Social Implications
13.5.1 Ethical Considerations
13.5.2 Legal Implications
13.5.3 Social Implications
13.5.4 Regulatory Frameworks and Compliance
13.6 Prospects for Generative AI and Customized Healthcare
13.7 Scalability and Adoption in Clinical Settings
13.7.1 Techniques for Effective Scalability and Acceptance
13.8 The Role of Interdisciplinary Collaboration
13.8.1 Encouraging All-Inclusive Resolutions
13.8.2 Improving Innovation by Utilizing Various Viewpoints
13.8.3 Surmounting Data Challenges
13.8.4 Encouraging the Acceptance and Application
13.8.5 Increasing the Use of Patient-Centered Care
13.9 Conclusion
Bibliography
14. Transforming Healthcare Infrastructure through Generative Artificial Intelligence: A Focus on Sustainability, Efficiency, and Patient Centricity
Sana Shabir Khan, Seema Sharma, Sheema Khan and Asif Hussain Shah
14.1 Introduction
14.1.1 IoT in Healthcare
14.2 AI in Healthcare Infrastructure
14.3 Introduction to Generative AI in Healthcare
14.4 Applications in Medical Diagnostics
14.4.1 Drug Discovery and Development
14.4.2 Virtual Health Assistants
14.4.3 Medical Research
14.5 Enhancing Efficiency and Patient Centricity
14.5.1 Transforming Healthcare Infrastructure
14.5.2 Case Study: AI-Driven Hospital Design
14.5.3 Case Study: Smart Clinics
14.5.4 Case Study: Predictive Analytics in Patient Care
14.5.5 Optimizing Drug Discovery Processes
14.5.6 Patient-Centric Virtual Health Assistants
14.6 Ensuring Sustainability in Healthcare Delivery
14.7 Ethical and Regulatory Challenges
14.8 Future Trends and Innovations
14.8.1 Case Studies and Success Stories
14.9 Infrastructure Design and AI Integration
14.9.1 Smart Cities and Healthcare Integration
14.9.1.1 Resilience and Disaster Preparedness
14.9.1.2 Data Analytics and Decision Support Systems
14.9.1.3 Regenerative and Sustainable Infrastructure
14.9.1.4 Ethical and Social Implications
14.9.1.5 Collaboration with Healthcare Professionals
14.10 Conclusion: Transforming Healthcare through Generative AI
Bibliography
15. Recent Deep Learning Techniques for Medical Image Diagnostics: Case Study on Segmentation of Exudates in Fundus Images
Jethe Krushi, Aritro Santra and Anu Shaju Areeckal
15.1 Fundamental Concepts
15.1.1 Linear Algebra
15.1.2 Conditional Probability
15.2 Data Preparation
15.2.1 Augmentation
15.2.2 Train-Validation-Test Sets
15.3 Deep Learning with Images
15.3.1 Classification Tasks
15.3.2 Segmentation Tasks
15.3.3 Generation Tasks
15.4 Deep Learning Architectures
15.4.1 Neural Networks
15.4.2 Convolutional Neural Networks (CNNs)
15.4.3 Vision Transformers
15.4.4 Generative Models
15.4.4.1 Generative Adversarial Networks (GANs)
15.4.4.2 Diffusion Models
15.4.5 Multi-Modal Architectures
15.5 Evaluation Metrics for Generative Tasks
15.5.1 Inception Score (IS)
15.5.2 Fréchet Inception Distance (FID)
15.5.3 Kernel Inception Distance (KID)
15.5.4 Precision
15.5.5 Recall
15.6 Case Study on Diabetic Retinopathy: Segmentation, Grading, and Data Augmentation Techniques
15.6.1 Segmentation of Exudates Using ResNet50 and SqueezeNet
15.6.2 Architectures from Literature for Enhancing Segmentation
15.6.3 Data Augmentation Using GANs
15.6.4 Using GANs for Segmentation and Classification
15.6.4.1 Repurposing GANs for Classification
15.6.4.2 Repurposing GANs for Segmentation
15.7 Conclusion
References
16. Deep Learning for EEG-Based Epileptic Seizure Detection: A Review
Mukesh Pandey, Latika Narayan, Pratibha Pandey, Upinder Kaur and Salil Bharany
16.1 Introduction
16.2 Literature Review
16.3 Methods for Epileptic Seizure Detection
16.3.1 Minimum Variance
16.3.2 Amplitude Thresholding
16.3.3 Lossless EEG Data Compression
16.3.4 Temporal Synchronization
16.3.5 Minimizing the False Alarm Rate
16.3.6 Common Spatial Pattern
16.4 Role of DL Techniques in ESD
16.4.1 Multi Perception–Based Seizure Detection
16.4.2 Focus Localization–Based Detection
16.4.3 Handcrafted-Based Detection
16.4.4 1D-CNN Technique–Based Detection
16.5 Challenges in Epileptic Seizure Detection
16.6 Conclusion and Future Direction
References
17. Smart Cane Development for the Visually Impaired:
Fusion of Sensor and Computer Vision Technologies
Chalachew Dagnew Yehuala, Rajan John, Nagarajan. S. and Umamaheswaran. S.
17.1 Introduction
17.1.1 Obstacles and Prospects for the Future
17.1.2 Knowledge Gap
17.1.3 Importance and Impact
17.1.4 Objectives
17.2 Related Work
17.3 Methodology
17.3.1 Operational Principles
17.4 Components
17.4.1 Object Detection and Recognition
17.4.1.1 Computer Vision–Based Solutions
17.4.1.2 Sensor-Based Solutions
17.4.2 Camera and Raspberry Pi Interfacing
17.4.2.1 Estimation of the Field of View (FOV)
17.4.2.2 Configuring the Camera with the Raspberry Pi
17.4.3 Ultrasonic Sensor Configuration
17.4.3.1 Sensor Placement and Configuration
17.4.3.2 Voltage Divider Circuit
17.4.4 Water Detection
17.4.4.1 Interfacing with Raspberry Pi
17.4.4.2 System Programming for Water Detection
17.4.5 Location Tracking
17.4.5.1 GPS Module Usage
17.4.5.2 Interfacing GSM/SIM 900A with Raspberry Pi
17.4.6 Power Source
17.4.6.1 Pi Sugar 2 Pro
17.4.7 Prototype Manufacturing
17.5 Empirical Preparations
17.5.1 Dataset and Augmentation
17.5.2 Training
17.6 Empirical Analysis and Evaluations
17.6.1 Implementation
17.6.2 Performance Discussion
17.7 Conclusion and Future Work
References
18. Generative Adversarial Networks in COVID-19 Image
Augmentation and Its Future Prospects: A Systematic Review
Radhika J.M., J. Ajayan, Sandip Bhattacharya and Sreejith. S.
18.1 Introduction
18.2 Different Types of Generative Adversarial Networks
18.3 Generative Adversarial Networks in COVID-19 Image Augmentation and Segmentation
18.4 Recent Application of Generative Adversarial Networks in Various Other Lung Image Augmentation and Segmentation Methods
18.5 Future Prospects of Generative Adversarial Networks
18.6 Conclusion
References
Index
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