Generative Artificial Intelligence for Biomedical and Smart Health Informatics

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 disorders
• Bio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systems
• Traffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoring
• Education-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.

Aditya Khamparia, Assistant Professor, Department of Computer Science at Babasaheb Bhimrao Ambedkar University, India. His research areas include Artificial Intelligence, Intelligent Data Analysis, Machine Learning, Deep Learning, and Soft Computing.

Deepak Gupta, Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India. His research interests include intelligent data analysis, nature-inspired computing, machine learning, and soft computing.