AI in Disease Detection: Advancements and Applications

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.

Table of Contents:
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 Pui-In Mak, 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