About the Editors xxvii List of Contributors xxix Preface xxxix Acknowledgments xli 1 Generative AI in Wearables: Exploring the Impact of GANs, VAEs, and Transformers 1 Diwakar Diwakar and Deepa Raj 1.1 Introduction 1 1.2 Theoretical Foundations 7 1.3 Opportunities of Integration 14 1.4 Research and Development Insights 16 1.5 Ethical and Regulatory Considerations 24 1.6 Case Studies and Applications 26 1.7 Future Directions and Emerging Trends 27 1.8 Conclusion 31 References 32 2 Safeguarding Privacy and Security in AI-Enabled Healthcare Informatics 35 Akanksha Kochhar, Ganeev Kaur Chhabra, Toshika Goswami, and Moolchand Sharma 2.1 Introduction 35 2.2 Drawbacks and Their Possible Solutions 38 2.3 Applications 43 2.4 Devices 44 2.5 Future Scope 46 2.6 Conclusion 47 2.7 Future Scope 48 References 49 3 Generating Synthetic Medical Data Using GAI 51 Sudhanshu Singh, Suruchi Singh, and C.S. Raghuvanshi 3.1 Introduction 51 3.2 Uncloaking the GAI Orchestra: A Compendium of Techniques 53 3.3 Beyond the Notes: Ethical Considerations and Responsible Use 66 3.4 Conclusion 70 References 70 4 Automation of Drug Design and Development 73 Sudhanshu Singh 4.1 Introduction 73 4.2 High-Throughput Screening (HTS) 74 4.3 Artificial Intelligence (AI) and Machine Learning (ML) 77 4.4 Automation in Drug Synthesis and Optimization 80 4.5 Automation in Clinical Trials 81 4.6 Challenges and Opportunities 83 4.7 Conclusion 85 References 87 5 Autism Spectrum Disorder Diagnosis: A Comprehensive Review of Machine Learning Approaches 89 Deepti Prasad and Suman Bhatia 5.1 Introduction 89 5.2 Machine Learning and Deep Learning Algorithms 92 5.3 Discussion 98 5.4 Future Work 99 5.5 Conclusion 99 References 100 6 Temporal Normalization and Brain Image Analysis for Early-Stage Prediction of Attention Deficit Hyperactivity Disorder (ADHD) 103 Poonam Chaudhary, Nikki Rani, Diksha Aggarwal, and Srishti Sharma 6.1 Introduction 103 6.2 Exploratory Data Analysis 105 6.3 Methodology 109 6.4 Results and Discussion 115 6.5 Conclusion 116 References 117 7 Sustainable Agriculture Through Advanced Crop Management: VGG16-Based Tea Leaf Disease Recognition 121 R. Sivaraman, S. Praveena, and H. Naresh Kumar 7.1 Introduction 121 7.2 Literature Survey 122 7.3 Proposed Methodology for Tea Leaf Diseases Detection 125 7.4 Results and Discussion 130 7.5 Conclusion 131 References 132 8 Advancing Colorectal Cancer Diagnosis: Integrating Synthetic Data and Machine Learning for Microbiome Analysis 135 Alessio Rotelli and Ernesto Iadanza 8.1 Colorectal Cancer (CRC) 135 8.2 Understanding the Gut Microbiome 136 8.3 Influence of the Gut Microbiome Dysbiosis on Colorectal Adenomas and CRC 136 8.4 Differentiating Adenomatous Polyps (AP) from CRC 137 8.5 Use of Data Augmentation 138 8.6 Data Evaluation Metrics 138 8.7 Feature Extraction by Later-Wise Relevance Propagation 139 8.8 Beta Diversity Analysis 140 8.9 Machine Learning and SHAP Analysis to Classify AP and CRC Samples 141 8.10 Results of Classification and SHAP Analysis 143 8.11 Key Bacterial Taxa Discriminating Between AP and CRC: Insights from Feature Extraction and SHAP Analysis 149 8.12 Conclusion 149 References 150 9 Recent Knowledge in Drug Design and Development: Automation and Advancement 153 Kusum Gurung, Saurav K. Mishra, Tabsum Chhetri, Sneha Roy, Anagha Balakrishnan, and John J. Georrge 9.1 Introduction 153 9.2 Automation in Drug Design and Development 156 9.3 Tools and Database for Drug Design, including Algorithm and Application 158 9.4 Automation in Drug Design and Its Impact on the Pharmaceutical Sector 160 9.5 Automation-Assisted Successful Studies in Drug Design 165 9.6 Advancement and Challenges 170 9.7 Conclusion 171 References 172 10 Machine Learning and Generative AI Techniques for Sentiment Analysis with Applications 183 Riya Sharma, Balraj Singh, and Aditya Khamparia 10.1 Introduction 183 10.2 Literature Review 187 10.3 Machine Learning Techniques for Sentiment Analysis 187 10.4 Generative AI Techniques for Sentiment Analysis 196 10.5 Conclusion 202 References 203 11 Use of AI with Optimization Techniques: Case Study, Challenges, and Future Trends 209 Ayushi Mittal, Parul Parul, Charu Gupta, and Devendra K. Tayal 11.1 Introduction 209 11.2 Overview of Medical Disease Prediction Models 213 11.3 Importance of Optimization in Enhancing Prediction Accuracy 214 11.4 Commonly Used Optimization Algorithms in Medical Predictive Modeling 214 11.5 Integration of ML and Optimization for Disease Prediction 222 11.6 Challenges and Considerations in Applying Optimization Techniques to Medical Data 223 11.7 Case Studies: Successful Applications of Optimization in Disease Prediction 226 11.8 Future Directions and Emerging Trends in Optimizing Medical Prediction Models 228 11.9 Ethical and Regulatory Implications of Optimized Disease Prediction Systems 231 11.10 Conclusion: Harnessing Optimization for Advancements in Medical Predictive Analytics 233 11.11 Future Scope 234 References 234 12 Inclusive Role of Internet of (Healthcare) Things in Digital Health: Challenges, Methods, and Future Directions 239 Mohammed Abdalla 12.1 Introduction 239 12.2 The Internet of Medical Things’ (IoMT) Revolution in Healthcare 242 12.3 The Integration Between Internet of (Healthcare) Things and Digital Health 243 12.4 Blockchain Applications in the Healthcare Systems 248 12.5 Healthcare IoT Future Directions: For Digital Health 249 12.6 Conclusion 252 References 253 13 Generating Synthetic Medical Dataset Using Generative AI: ACaseStudy 259 Partha Pratim Ray 13.1 Introduction 259 13.2 Methodology 260 13.3 Results 265 13.4 Conclusion 270 References 270 14 A Comprehensive Review of Cardiac Image Analysis for Precise Heart Disease Diagnosis Using Deep Learning Techniques 275 Anuj Gupta, Vikas Kumar, and Aryan Nakhale 14.1 Introduction 275 14.2 Literature Review 276 14.3 Machine Learning Methods 278 14.4 Proposed System 279 14.5 Mathematical Model 282 14.6 Data Preparation 284 14.7 Results and Discussion 286 14.8 Conclusion and Future Work 292 References 293 15 Classification Methods of Deep Learning for Detecting Autism Spectrum Disorder in Children (4–12 Years) 297 Yashashwini Reddy, Chinthala Kishor Kumar Reddy, Kari Lippert, and Sahithi Reddy 15.1 Introduction 297 15.2 Relevant Work 302 15.3 Proposed Methodology 305 15.4 Results 312 15.5 Conclusion 314 References 317 16 Deep Learning Model for Resolution Enhancement of Biomedical Images for Biometrics 321 Bhallamudi RaviKrishna, Madireddy Vijay Reddy, Mukesh Soni, Haewon Byeon, Sagar D. Pande, and Maher A. Rusho 16.1 Introduction 321 16.2 Model 324 16.3 Experiments and Results 332 16.4 Conclusion 338 References 338 17 Tackling the Complexities of Federated Learning 343 Raj Thakur, Shreyansh Patel, Neelesh Singh, Aaryan Barde, and Snehlata Barde 17.1 Introduction 343 17.2 Why We Come to Federated Learning 344 17.3 Related Work 344 17.4 Challenges in Federated Learning 345 17.5 Techniques Used in Federated Learning 347 17.6 Applications 350 17.7 Result and Analysis 351 17.8 Conclusion 351 References 352 18 Revolutionizing Healthcare: The Impact of AI-Powered Sensors 355 Veenadhari Bhamidipaty, Durgananda Lahari Bhamidipaty, Indira Guntoory, Kanaka Durga Prasad Bhamidipaty, Karthikeyan P. Iyengar, Bhuvan Botchu, and Rajesh Botchu 18.1 Introduction 355 18.2 Evolution of Healthcare Technology 356 18.3 Understanding AI-Powered Sensors 358 18.4 Enhancing Patient Monitoring and Diagnosis 359 18.5 Improving Treatment Outcomes 361 18.6 Remote Healthcare and Telemedicine 362 18.7 Challenges and Ethical Considerations 363 18.8 Regulatory Landscape 365 18.9 Future Directions and Opportunities 366 18.10 Case Studies and Success Stories 367 References 370 19 GAI and Deep Learning-Based Medical Sensor Data Relationship Model for Health Informatics 375 Kirti Shukla, Pramod Kumar, Mukesh Soni, Haewon Byeon, Sagar Dhanraj Pande, and Ismail Keshta 19.1 Introduction 375 19.2 Related Work 379 19.3 DSRF Based on Dynamic and Static Relationships Fusion of Multisource Health Sensing Data 381 19.4 Experiments and Analysis 388 19.5 Conclusion 397 References 397 20 Leveraging Generative Adversarial Networks for Image Augmentation in Deep Learning 401 Ravi Kumar, Akshay Kanwar, Amritpal Singh, and Aditya Khamparia 20.1 Introduction 401 20.2 Literature Review 403 20.3 Material and Method 411 20.4 Result and Discussion 413 20.5 Conclusion 414 References 414 21 Exploring Trust and Mistrust Dynamics: Generative Ai-curated Narratives in Health Communication Media Content Among Gen X 417 Seema Shukla, Babita Pandey, Devendra Kumar Pandey, Brijendra Pratap Mishra, and Aditya Khamparia 21.1 Background 417 21.2 Related Work 418 21.3 Theoretical Framework 420 21.4 Research Methodology 420 21.5 Data Analysis 423 21.6 Results 424 21.7 Conclusions and Discussion 428 References 430 22 Generative Intelligence-Based Federated Learning Model for Brain Tumor Classification in Smart Health 435 Niladri Maiti, Riddhi Chawla, Aadam Quraishi, Mukesh Soni, Maher Ali Rusho, and Sagar Dhanraj Pande 22.1 Introduction 435 22.2 Classification Model 438 22.3 Experiment 444 22.4 Conclusion 449 References 450 23 AI-Based Emotion Detection System in Healthcare for Patient 455 Ati Jain and Amiyavardhan Jain 23.1 Introduction 455 23.2 Literature Survey 456 23.3 AI in Healthcare Sector 458 23.4 Methodology 465 23.5 Conclusion 465 References 467 24 Leveraging Process Mining for Enhanced Efficiency and Precision in Healthcare 471 Parth Sharma, Sohan Kumar, Tanay Falor, Om Dabral, Abhinav Upadhyay, Rishik Gupta, and Vanshika Singh Andotra 24.1 Introduction 471 24.2 Process Mining 472 24.3 Main Focus of the Chapter 474 24.4 Problems 476 24.5 Solution 476 24.6 Tools 477 24.7 Ways Process Mining Solves Healthcare 479 24.8 One Solution: Robotic Process Automation (RPA) 482 24.9 Case Study: Process Mining for Optimized COVID-19 ICU Care 483 24.10 Conclusion 486 References 487 25 Transform Drug Discovery and Development With Generative Artificial Intelligence 489 Antonio Lavecchia 25.1 Introduction 489 25.2 Dataset, Molecular Representation, and Benchmark Platforms in Molecular Generation 491 25.3 Deep Generative Model Architectures 499 25.4 AI Applications in Drug Discovery and Development 511 25.5 Challenges and Future Outlooks 516 Acknowledgments 519 References 520 26 Medical Image Analysis and Morphology with Generative Artificial Intelligence for Biomedical and Smart Health Informatics 539 Dharmendra Dangi, Arish Mallick, Amit Bhagat, and Dheeraj Kumar Dixit 26.1 Introduction 539 26.2 Medical Imaging 541 26.3 Various Types of Modalities 543 26.4 Medical Imaging Analysis 549 26.5 Conventional Morphological Image Processing 551 26.6 Rotational Morphological Processing 553 References 560 27 Machine Learning Applications in the Prediction of Polycystic Ovarian Syndrome 565 Ardra Nair, Virrat Devaser, and Komal Arora 27.1 Introduction 565 27.2 Literature Review 569 27.3 ml Techniques for Polycystic Ovarian Syndrome 569 27.4 Artificial Neural Network and Deep Learning 580 27.5 Challenges 584 27.6 Conclusion 585 References 585 28 Diagnosis and Classification of Skin Cancer Using Generative Artificial Intelligence (Gen AI) 591 Niveditha N. Reddy and Pooja Agarwal 28.1 Introduction 591 28.2 Factors Affecting Skin Cancer Detection 592 28.3 Different Types of Skin Cancer 592 28.4 How Common Is Skin Cancer? 592 28.5 Dermatological Images and Datasets 595 28.6 Datasets 599 28.7 Skin Cancer Classification in Typical CNN Frameworks 599 28.8 Imbalance in Data and Limitations in Disease in Skin Databases 600 28.9 ml Techniques for Skin Cancer Diagnosis 601 28.10 Conclusion 604 References 604 29 Secure Decentralized ECG Prediction: Balancing Privacy, Performance, and Heterogeneity 607 Bagesh Kumar, Sohan Kumar, Yash Vikram Singh Rathore, Akash Raj, Vanshika Singh Andotra, Rishik Gupta, and Prakhar Shukla 29.1 Introduction 607 29.2 Parsing ECG Data 609 29.3 FL for Decentralized ECG Prediction 612 29.4 Security and Privacy in FL 613 29.5 Addressing Heterogeneity in ECG Dataset 615 29.6 Case Study: Advancing Heart Disease Prediction with Asynchronous Federated Deep Learning 617 29.7 Conclusion 619 References 619 Index 623

Enables readers to understand the future of medical applications with generative AI and related applications Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context. The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices. Topics covered include: Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disordersBio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systemsTraffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoringEducation-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.