AI in Disease Detection
Advancements and Applications
1st Edition
Published on 31. December 2024
Book
Hardback
400 pages
978-1-394-27866-4 (ISBN)
Description
Comprehensive resource encompassing recent developments, current use cases, and future opportunities for AI in disease detection
AI in Disease Detection discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.
This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare.
Sample topics explored in AI in Disease Detection include:
Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data
Identification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics
AI's role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios
Clinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness
Delivering excellent coverage of the subject, AI in Disease Detection is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.
AI in Disease Detection discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.
This book assists readers in assessing big data in healthcare and determining the drawbacks and possibilities associated with the implementation of AI in disease detection; categorizing major applications of AI in disease detection such as cardiovascular disease detection, cancer diagnosis, neurodegenerative disease detection, and infectious disease control, as well as implementing distinct AI methods and algorithms with medical data including patient records and medical images, and understanding the ethical and social consequences of AI in disease detection such as confidentiality, bias, and accessibility to healthcare.
Sample topics explored in AI in Disease Detection include:
Legal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data
Identification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics
AI's role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios
Clinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness
Delivering excellent coverage of the subject, AI in Disease Detection is an essential up-to-date reference for students, healthcare professionals, academics, and practitioners seeking to understand the possible applications of AI in disease detection and stay on the cutting edge of the most recent breakthroughs in the field.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 22 mm
Weight
703 gr
ISBN-13
978-1-394-27866-4 (9781394278664)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
12/2024
1st Edition
Wiley
€107.99
Available for download
E-Book
12/2024
1st Edition
Wiley
€110.99
Available for download
Persons
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.
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.
Content
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
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