Rajesh Singh & Anita Gehlot 
AI in Disease Detection [EPUB ebook] 
Advancements and Applications

About the Editors xix

List of Contributors xxi

Preface xxiii

Acknowledgments xxv

1 Introduction to AI in Disease Detection — An Overview of the Use of AI in Detecting Diseases, Including the Benefits and Limitations of the Technology 1
Arvind Singh Rawat, Jagadheswaran Rajendran, and Shailendra Singh Sikarwar

Introduction 1

Objectives 2

Literature Review 4

Benefits of AI in Disease Detection 7

Limitations of AI in Disease Detection 9

AI Techniques in Disease Detection 10

Supervised Learning for Disease Diagnosis 10

Unsupervised Learning in Healthcare 10

Deep Learning and Convolutional Neural Networks (CNNs) 11

AI in Medical Imaging and Radiology 11

Applications of AI in Disease Detection 12

Oncology: Cancer Detection and Diagnosis 12

Cardiology: Predicting Cardiovascular Diseases 12

Neurology: Early Detection of Neurological Disorders 12

Infectious Diseases: AI in Epidemic and Pandemic Management 13

Methodology 13

Data Collection and Preprocessing 13

Multimodal Fusion Techniques 14

Transfer Learning for Disease Detection 14

Explainable AI (XAI) Techniques 14

Federated Learning Framework 14

Clinical Validation and Adoption Studies 16

Continuous Monitoring and Early Warning Systems 16

Results and Analysis 16

Analysis 17

Performance Evaluation for the Techniques of Multimodal Fusion 17

Assessment of Transfer Learning for Disease Detection 18

Effectiveness of Explainable AI Techniques 18

Privacy-Preserving Federated Learning-Based Collaborative Model Training 18

Performance of Continuous Monitoring and Early Warning Systems 19

Case Study: AI in Disease Detection 20

Development and Training 20

Testing and Validation 20

Deployment and Integration 21

Conclusion 22

Future Scope 23

References 24

2 Explanation of Machine Learning Algorithms Used in Disease Detection, Such as Decision Trees and Neural Networks 27
Nikhil Verma, Tripti Sharma, and Bobbinpreet Kaur

Introduction 27

The Silent Guardian: Machine Learning’s Stealthy Rise in Disease Detection 27

Beyond the Usual Suspects: A Look at Emerging Innovations 27

The Ethical Symphony: Balancing Innovation with Human Oversight 28

Objectives 28

Unveiling Hidden Patterns – Feature Engineering 28

Innovation Spotlight: Active Feature Acquisition (AFA) 29

Limitations and Advantages of ML Algorithms for Disease Detection 30

Advantages of Machine Learning Algorithms for Disease Detection 31

Limitations of Machine Learning Algorithms for Disease Detection 31

Literature Review 32

The Familiar Melodies: Established ML Techniques and Their Strengths 33

The Rise of the Deep Learning Chorus: Innovation on the Horizon 33

Breaking New Ground: Unveiling Unique Innovations and Addressing Challenges 38

The Well-Honed Orchestra: Established Techniques Take Center Stage 38

Beyond the Familiar Melodies: Deep Learning Takes the Stage 39

Collaboration and Innovation Lead the Way 40

Methodology 40

Conventional ML Methods for Disease Detection 41

Beyond the Established Melodies: Innovation Takes Center Stage 42

Results and Analysis 43

The Familiar Melody: Established Methodologies 43

The Disruptive Score: Unveiling New Innovations 44

The Human Touch: Ethical Considerations and Explainability 45

Conclusions and Future Scope 45

The Evolving Maestro: AI Orchestration Beyond Established Methods 46

Human-Machine Duet: Collaboration for a Healthier Future 46

References 47

3 Natural Language Processing (NLP) in Disease Detection — A Discussion of How NLP Techniques Can Be Used to Analyze and Classify Medical Text Data for Disease Diagnosis 53
Vinod Kumar, Mohammed Ismail Iqbal, and Rachna Rathore

Introduction 53

Objectives 54

Early Infection Location through Phonetic Fingerprints 54

Estimation Examination for All-Encompassing Healthcare 55

Social Media Reconnaissance for Disease Outbreaks 55

Custom-Fitted Medication through Personalized Content Investigation 55

Precise Medication with Clinical Trial Content Mining 56

Breaking Down Language Boundaries for Worldwide Wellbeing 56

Human-Machine Collaboration for Making Strides 56

Advantages and Limitations of Natural Language Processing in Disease Detection 57

Advantages of NLP in Disease Detection 57

Limitations of NLP in Disease Detection 58

Literature Review 59

From Content to Determination: Revealing Etymological Fingerprints 59

Past Watchwords: Capturing the Subtlety of Free-Text Information 59

Control of Expansive Language Models: A New Frontier 59

Breaking Down Language Obstructions for Worldwide 61

Toward a Collaborative Future: Human-Machine Association 61

Logical AI 61

Past Content: Multimodal Infection Discovery with NLP and Imaging Information 62

Methodology 62

Information Procurement and Preprocessing: Building the Establishment 62

Content Explanation: Labeling the Story 63

Feature Designing: Extricating Important Signals 63

Show Determination and Preparing: Choosing the Right Tool for the Work 63

Demonstrate Assessment and Refinement: Guaranteeing Exactness and Belief 63

Integration and Arrangement: Putting NLP to Work 64

Results and Analysis 64

Current Achievements: A Glimpse into the Possible 64

Unveiling New Frontiers: Innovative Approaches for the Future 66

Challenges and Considerations: Navigating the Road Ahead 66

Case Study of NLP in Disease Detection 67

Conclusions and Future Scope 69

Charting the Course: Unveiling New Frontiers in NLP 70

A Collaborative Future: Working Together for a Healthier Tomorrow 70

Enhancing EHR Analysis 71

Personalized Pharmaceutical 71

Integration with AI and Machine Learning 72

Expansion into New Medical Fields 72

Upgrading Persistent Engagement 72

Ethical and Protection Contemplations 73

References 73

4 Computer Vision for Disease Detection — An Overview of How Computer Vision Techniques Can Be Used to Detect Diseases in Medical Images, Such as X-Rays and MRIs 77
Ravindra Sharma, Narendra Kumar, and Vinod Sharma

Introduction 77

Objectives 78

Improved Early Disease Detection 78

Improve Diagnostic Accuracy 78

Developing Transfer Learning Models for Medical Imaging 78

Explainability in Artificial Intelligence Applied to Medical Imaging 79

Building Computer-Vision-Based Real-Time Disease Diagnostics Systems 79

Integration of Multimodal Data for Comprehensive Diagnosis 79

Literature Review 79

Improving Early Detection and Diagnostic Accuracy 80

Switch Studying and Artificial Records Generation 80

Explainable AI and Real-Time Detection Structures 80

Multimodal Statistics Integration 81

Innovations in Precise Disease Detection 81

Advanced Deep Learning Strategies 83

Statistics Augmentation and Synthesis 83

Explainable AI for Trust and Transparency 83

Real-Time Diagnostic Systems 84

Integration of Multimodal Insights 84

Disease-Specific Innovations 84

Benefits of AI in Disease Detection 85

Limitations of AI in Disease Detection 86

Methodology 87

Records Series and Preprocessing 87

Version Improvement 88

Real-Time Processing and Deployment 88

Multimodal Records Integration 89

Continuous Mastering and Development 89

Results and Analysis 89

Diagnostic Accuracy 91

Efficiency and Pace 91

Explainability and Agreement 92

Multimodal Statistics Integration 92

Key Improvements 92

Continuous Learning and Variation 93

Medical Integration and Impact 93

Key Improvements 93

Conclusion and Future Scope 94

References 96

5 Deep Learning for Disease Detection — A Deep Dive into Deep Learning Techniques Such as Convolutional Neural Networks (CNNs) and Their Use in Disease Detection 99
Mohammed Ismail Iqbal and Priyanka Kaushik

Introduction 99

Objectives 100

Literature Review 101

Integration of Multimodal Information 102

Switch Learning for Better Model Training 102

Explainable AI Techniques for CNNs 102

Records Augmentation and Synthesis Techniques 103

Fundamentals of Deep Learning 105

CNNs in Medical Imaging 106

Image Processing for Disease Detection 107

Methodology 109

Convolutional Neural Networks: A Top-Level View 109

Multiscale Convolutional Layers 109

Attention Mechanisms 109

Transfer Learning with Pretrained Models 110

Generative Adversarial Networks (GANs) for Statistics Augmentation 110

Self-Supervised Learning 110

Results and Analysis 111

Accuracy and Performance 112

Enhanced Diagnostic Accuracy 112

Sensitivity and Specificity 113

Speed and Efficiency 113

Reliability and Consistency 113

Effects 114

Multiscale Convolutional Layers 114

Attention Mechanisms 115

Switch Learning with Pretrained Models 115

GANs for Statistics Augmentation 115

Self-Supervised Learning 115

Improved Diagnostic Accuracy and Performance 115

Reduced Dependence on Massive Labeled Datasets 116

Better Version Robustness and Generalization 116

Scalability and Flexibility 116

Innovations and Future Instructions 116

Multimodal Gaining Knowledge 116

Federated Learning for Privateness-Retaining AI 116

Explainable AI (XAI) for Stepped Forward Interpretability 116

Integration with Wearable Devices 117

Real-Time Adaptive Learning 117

Conclusion and Future Scope 117

Multimodal Deep Learning Integration 118

Federated Learning for Stronger Privacy 118

Explainable AI (XAI) for Transparency 118

Wearable Generation AI and Continuous Monitoring 119

Adaptive Learning and Real-Time Model Updating 119

Personalized Remedy and Predictive Analytics 119

Collaborative AI Systems 119

Stronger Data Augmentation Techniques 119

AI-Driven Clinical Trials and Research 120

International Health and AI-Driven Disorder Surveillance 120

References 120

6 Applications of AI in Cardiovascular Disease Detection — A Review of the Specific Ways in which AI Is Being Used to Detect and Diagnose Cardiovascular Diseases 123
Satish Mahadevan Srinivasan and Vinod Sharma

Introduction 123

Objectives 124

Literature Review 126

Fundamentals of AI in Medical Applications 129

Machine Learning vs. Deep Learning 129

AI Techniques for Cardiovascular Disease Detection 131

Convolutional Neural Networks (CNNs) 131

Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks 131

Support Vector Machines (SVMs) 132

Random Forests 132

AI in Cardiovascular Imaging 132

AI in Echocardiography 133

AI in Cardiac MRI and CT Scans 133

AI in Nuclear Cardiology 133

AI in Electrocardiogram (ECG) Analysis 134

Computer-Based ECG Interpretation 134

Case Studies and Real-World Implementations 134

AI in Risk Prediction and Stratification 135

Risk Prediction Models 135

Personalized Risk Stratification 136

AI in Monitoring and Managing Cardiovascular Health 136

AI-Assisted Disease Management 137

Challenges and Limitations of AI in Cardiovascular Disease Detection 137

Data Quality and Availability 137

Model Interpretability and Transparency 138

Clinical Integration and Adoption 138

Ethical and Legal Considerations 138

Methodology 139

Results and Analysis 140

Conclusion and Future Scope 142

References 144

7 Applications of AI in Cancer Detection — A Review of the Specific Ways in which AI Is Being Used to Detect and Diagnose Various Types of Cancer 147
Shival Dubey and Shailendra Singh Sikarwar

Introduction 147

Objectives 148

Literature Review 150

Methodology 159

Results and Analysis 160

Conclusion and Future Scope 162

References 163

8 Applications of AI in Neurological Disease Detection — A Review of Specific Ways in Which AI Is Being Used to Detect and Diagnose Neurological Disorders, Such as Alzheimer’s and Parkinson’s 167
Dolly Sharma and Priyanka Kaushik

Introduction 167

Objectives 168

Literature Review 169

Key Applications of AI in Medical Settings 180

AI Techniques for Detecting Alzheimer’s Disease 181

AI Techniques for Detecting Parkinson’s Disease 181

AI Techniques in Other Neurological Disorders 182

Methodology 183

Results and Analysis 184

Conclusion and Future Scope 186

References 187

9 AI Integration in Healthcare Systems — A Review of the Problems and Potential Associated with Integrating AI in Healthcare for Disease Detection and Diagnosis 191
Praveen Kumar Malik, Hitesh Bhatt, and Madhuri Sharma

Introduction 191

Objectives 192

Literature Review 194

Advantages of AI Integration in Healthcare Systems for Disease Detection and Diagnosis 197

Limitations of AI Integration in Healthcare Systems for Disease Detection and Diagnosis 199

Applications of AI Integration in Healthcare Systems for Disease Detection and Diagnosis 200

Methodology 203

Results and Analysis 205

More Desirable Diagnostic Accuracy and Efficiency 205

Interpretability and Trustworthiness 205

Robustness and Generalizability 207

Continuous Learning and Version 207

Patient Consequences and Healthcare Impact 207

Observations 208

Potential Benefits of AI Integration 208

Future Directions 209

Conclusion 209

Future Scope 210

References 212

10 Clinical Validation of AI Disease Detection Models — An Overview of the Clinical Validation Process for AI Disease Detection Models, and How They Can Be Validated for Accuracy and Effectiveness 215
Manish Prateek and Saurabh Pratap Singh Rathore

Introduction 215

Objectives 217

Literature Review 219

Advantages of the Clinical Validation of AI Disease Detection Models 223

The Clinical Validation Process 223

Clinical Trials 223

Limitations of the Clinical Validation Process 224

Data Quality and Availability 224

Model Generalizability 225

Regulatory and Ethical Challenges 225

Integration with Clinical Workflow 225

Cost and Resource Requirements 225

Interpretability and Transparency 225

Clinical Trial Limitations Narrow Focus 225

Applications of AI Disease Detection Models 226

Radiology and Medical Imaging 226

Pathology 226

Cardiology 226

Ophthalmology 228

Oncology 228

Neurology 228

Primary Care 228

Public Health 228

Research and Development 229

Methodology 229

Results and Analysis 230

Conclusion and Future Scope 233

References 235

11 Integration of AI in Healthcare Systems — A Discussion of the Challenges and Opportunities of Integrating AI in Healthcare Systems for Disease Detection and Diagnosis 239
Nitin Sharma and Priyanka Kaushik

Introduction 239

Objectives 240

Literature Review 242

Advantages of AI Integration in Healthcare Systems 245

Enhanced Diagnostic Accuracy 245

Early Disease Detection 245

Continuous Learning and Improvement 246

Limitations and Challenges of Integrating AI in Healthcare Systems 247

Applications of AI in Healthcare for Disease Detection and Diagnosis 250

Medical Imaging Analysis 250

Pathology: 4, 444 AI Systems Checking Biopsy Samples for Cancer Cells 250

Chronic Disease Management 252

Methodology 252

Results and Analysis 253

More Desirable Diagnostic Accuracy and Efficiency 253

Interpretability and Trustworthiness 254

Patient Outcomes and Healthcare Impact 256

Observations 256

Conclusion 259

Future Scope 259

Growth into Multi-Omics Records Integration 259

Development of AI-Driven Predictive Analytics for Physical Fitness 260

Enhancement of Real-Time Data Selection Guide Structures 260

Implementation of AI in Virtual and Telehealth Services 260

Ethical AI and Bias Mitigation Strategies 260

Collaborative AI for Interdisciplinary Studies 260

Personalized Fitness Training and Lifestyle Interventions 261

Augmented Reality (AR) and AI for Better Clinical Training 261

References 261

12 The Future of AI in Disease Detection — A Look at Emerging Trends and Future Directions in the Use of AI for Disease Detection and Diagnosis 265
Binboga Siddik Yarman and Saurabh Pratap Singh Rathore

Introduction 265

Objectives 266

Literature Review 268

Advantages of AI in Disease Detection 271

Limitations of AI in Disease Detection 273

Applications of AI in Disease Detection 275

Methodology 277

Result and Analysis 280

Observations 283

Upgraded Diagnosis Accuracy 283

Moving Toward Personalized Treatment 283

Advances in Foundation Imaging 284

Conclusion and Future Scope 285

References 286

13 Limitations and Challenges of AI in Disease Detection — An Examination of the Limitations and Challenges of AI in Disease Detection, Including the Need for Large Datasets and Potential Biases 289
Anchit Bijalwan and Shailendra Singh Sikarwar

Introduction 289

Objectives 290

Literature Review 292

Advantages of AI in Disease Detection: A Comprehensive Overview 295

Enhanced Accuracy and Precision 295

Speedier Preparing and Determination 295

Taking Care of Expansive Volumes of Information 295

Ceaseless Learning and Enhancement 296

Diminishment of Human Mistake 296

Limitations and Challenges of AI in Disease Detection 297

Applications of AI in Disease Detection: A Comprehensive Overview 299

Medical Imaging Analysis 299

Drug Discovery and Development 300

Methodology 302

Result and Analysis 303

Observations 306

Significant Impact on Medical Imaging 306

Automation and Efficiency in Pathology 306

Advancements in Genomics and Personalized Medicine 306

Early Detection and Proactive Health Management 306

Predictive Analytics for Risk Assessment 307

Support for Healthcare Professionals 307

NLP in Electronic Health Records 307

Enhancing Remote Monitoring and Telemedicine 307

Accelerating Drug Discovery 307

Addressing Mental Health 308

Conclusion and Future Scope 308

References 309

14 AI-Assisted Diagnosis and Treatment Planning — A Discussion of How AI Can Assist Healthcare Professionals in Making More Accurate Diagnoses and Treatment Plans for Diseases 313
Mamoon Rashid and Madhuri Sharma

Introduction 313

Objectives 315

Literature Review 316

Advantages of AI-Assisted Diagnosis and Treatment Planning 319

Advanced Diagnostic Accuracy 319

Personalized Treatment Plans 320

Efficient Data Management 320

Continuous Learning and Improvement 320

Predictive Analytics 320

Efficient Workflow 320

Support for Rural and Underserved Areas 321

Limitations of AI-Assisted Diagnosis and Treatment Planning 321

Concerns with Data Privacy and Security 321

Data Quality and Bias 321

Lack of Interpretability 322

Good-Quality Data 322

Integration with Existing Systems 322

Ethical and Legal Issues 322

Resistance to Change 323

Limited Clinical Validation 323

Summary of Challenges 323

Applications of AI-Assisted Diagnosis and Treatment Planning 323

Therapeutic Imaging Examination 325

Personalized Medicine 325

Predictive Analytics for Disease Prevention 325

Discovery and Development of New Drugs 326

Virtual Health Assistants 326

Robotic Surgery 326

Clinical Decision Support Systems (CDSS) 326

Remote Monitoring and Telemedicine 327

Optimizing Workflows 327

Methodology 327

Observations 328

Results and Analysis 331

Conclusion and Future Scope 333

References 334

15 AI in Disease Surveillance — An Overview of How AI Can Be Used in Disease Surveillance and Outbreak Detection in Real-World Scenarios 337
Abhishek Tripathi and Rachna Rathore

Introduction 337

Objectives 338

Literature Review 340

Advantages of AI in Disease Surveillance 343

Limitations of AI in Disease Surveillance 345

Information Quality and Accessibility 345

Protection and Security Concerns 345

Inclination in AI Calculations 345

Interpretability and Straightforwardness 345

Ethical and Legitimate Issues 345

Foundation and Asset Imperatives 346

Versatility to Advancing Dangers 346

Untrue Positives and Negatives 346

Real-World Case Thinks About Highlighting Confinements Google Flu Patterns (GFT) 346

Challenges in Low-Resource Settings 346

Inclination in Predictive Models 347

Applications of AI in Disease Surveillance 347

Early Detection Systems 347

Predictive Modeling 347

Computerized Information Collection and Integration 349

Real-Time Reconnaissance 349

Natural Language Programming (NLP) 349

Geospatial Investigation 349

Contact Tracking 349

Social Media Investigation 349

Methodology 350

Result and Analysis 351

Observations 354

Comprehensive Experiences 354

Key Perceptions Upgraded Early Discovery 354

Precise Predictive Modeling 354

Real-Time Checking 355

NLP Capabilities 355

Geospatial Examination and Mapping 355

Improved Contact Tracking 355

Opinion and Behavioral Examination 355

Challenges and Considerations 356

Data Quality and Availability 356

Protection and Ethical Concerns 356

Predisposition in AI Models 356

Interpretability and Straightforwardness 356

Foundation and Asset Imperatives 356

Conclusion and Future Scope 357

References 358

Index 361

Dr. Rajesh Singh, Professor, Electronics & Communication Engineering and Director, Research & Innovation, Uttaranchal University, India. Dr. Singh was featured among the top ten inventors in 2010 to 2020 by Clarivate Analytics in “India’s Innovation Synopsis” in March 2021.
Dr. Anita Gehlot, Professor, Electronics & Communication Engineering and Head -Research and Innovation, Uttaranchal University, India.
Dr. Navjot Rathour, Associate Professor, Electronics & Communication Engineering, Chandigarh University, Mohali, India.
Dr. Shaik Vaseem Akram, Assistant Professor, Electronics & Communication Engineering, S R University, Telangana, India.