ty and Flexibility -- Innovations and Future Instructions -- Multimodal Gaining Knowledge -- Federated Learning for Privateness-Retaining AI -- Explainable AI (XAI) for Stepped Forward Interpretability -- Integration with Wearable Devices -- Real-Time Adaptive Learning -- Conclusion and Future Scope -- Multimodal Deep Learning Integration -- Federated Learning for Stronger Privacy -- Explainable AI (XAI) for Transparency -- Wearable Generation AI and Continuous Monitoring -- Adaptive Learning and Real-Time Model Updating -- Personalized Remedy and Predictive Analytics -- Collaborative AI Systems -- Stronger Data Augmentation Techniques -- AI-Driven Clinical Trials and Research -- International Health and AI-Driven Disorder Surveillance -- References -- 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 -- Objectives -- Literature Review -- Fundamentals of AI in Medical Applications -- Machine Learning vs. Deep Learning -- AI Techniques for Cardiovascular Disease Detection -- Convolutional Neural Networks (CNNs) -- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks -- Support Vector Machines (SVMs) -- Random Forests -- AI in Cardiovascular Imaging -- AI in Echocardiography -- AI in Cardiac MRI and CT Scans -- AI in Nuclear Cardiology -- AI in Electrocardiogram (ECG) Analysis -- Computer-Based ECG Interpretation -- Case Studies and Real-World Implementations -- AI in Risk Prediction and Stratification -- Risk Prediction Models -- Personalized Risk Stratification -- AI in Monitoring and Managing Cardiovascular Health -- AI-Assisted Disease Management -- Challenges and Limitations of AI in Cardiovascular Disease Detection -- Data Quality and Availability -- Model Interpretability and Transparency -- Clinical Integration and Adoption -- Ethical and Legal Considerations -- Methodology -- Results and Analysis -- Conclusion and Future Scope -- References -- 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 -- Objectives -- Literature Review -- Methodology -- Results and Analysis -- Conclusion and Future Scope -- References -- 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 -- Objectives -- Literature Review -- Key Applications of AI in Medical Settings -- AI Techniques for Detecting Alzheimer's Disease -- AI Techniques for Detecting Parkinson's Disease -- AI Techniques in Other Neurological Disorders -- Methodology -- Results and Analysis -- Conclusion and Future Scope -- References -- 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 -- Objectives -- Literature Review -- Advantages of AI Integration in Healthcare Systems for Disease Detection and Diagnosis -- Limitations of AI Integration in Healthcare Systems for Disease Detection and Diagnosis -- Applications of AI Integration in Healthcare Systems for Disease Detection and Diagnosis -- Methodology -- Results and Analysis -- More Desirable Diagnostic Accuracy and Efficiency -- Interpretability and Trustworthiness -- Robustness and Generalizability -- Continuous Learning and Version -- Patient Consequences and Healthcare Impact -- Observations -- Potential Benefits of AI Integration -- Future Directions -- Conclusion -- Future Scope -- References -- 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 -- Objectives -- Literature Review -- Advantages of the Clinical Validation of AI Disease Detection Models -- The Clinical Validation Process -- Clinical Trials -- Limitations of the Clinical Validation Process -- Data Quality and Availability -- Model Generalizability -- Regulatory and Ethical Challenges -- Integration with Clinical Workflow -- Cost and Resource Requirements -- Interpretability and Transparency -- Clinical Trial Limitations Narrow Focus -- Applications of AI Disease Detection Models -- Radiology and Medical Imaging -- Pathology -- Cardiology -- Ophthalmology -- Oncology -- Neurology -- Primary Care -- Public Health -- Research and Development -- Methodology -- Results and Analysis -- Conclusion and Future Scope -- References -- 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 -- Objectives -- Literature Review -- Advantages of AI Integration in Healthcare Systems -- Enhanced Diagnostic Accuracy -- Early Disease Detection -- Continuous Learning and Improvement -- Limitations and Challenges of Integrating AI in Healthcare Systems -- Applications of AI in Healthcare for Disease Detection and Diagnosis -- Medical Imaging Analysis -- Pathology: 4,444 AI Systems Checking Biopsy Samples for Cancer Cells -- Chronic Disease Management -- Methodology -- Results and Analysis -- More Desirable Diagnostic Accuracy and Efficiency -- Interpretability and Trustworthiness -- Patient Outcomes and Healthcare Impact -- Observations -- Conclusion -- Future Scope -- Growth into Multi-Omics Records Integration -- Development of AI-Driven Predictive Analytics for Physical Fitness -- Enhancement of Real-Time Data Selection Guide Structures -- Implementation of AI in Virtual and Telehealth Services -- Ethical AI and Bias Mitigation Strategies -- Collaborative AI for Interdisciplinary Studies -- Personalized Fitness Training and Lifestyle Interventions -- Augmented Reality (AR) and AI for Better Clinical Training -- References -- 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 -- Objectives -- Literature Review -- Advantages of AI in Disease Detection -- Limitations of AI in Disease Detection -- Applications of AI in Disease Detection -- Methodology -- Result and Analysis -- Observations -- Upgraded Diagnosis Accuracy -- Moving Toward Personalized Treatment -- Advances in Foundation Imaging -- Conclusion and Future Scope -- References -- 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 -- Objectives -- Literature Review -- Advantages of AI in Disease Detection: A Comprehensive Overview -- Enhanced Accuracy and Precision -- Speedier Preparing and Determination -- Taking Care of Expansive Volumes of Information -- Ceaseless Learning and Enhancement -- Diminishment of Human Mistake -- Limitations and Challenges of AI in Disease Detection -- Applications of AI in Disease Detection: A Comprehensive Overview -- Medical Imaging Analysis -- Drug Discovery and Development -- Methodology -- Result and Analysis -- Observations -- Significant Impact on Medical Imaging -- Automation and Efficiency in Pathology -- Advancements in Genomics and Personalized Medicine -- Early Detection and Proactive Health Management -- Predictive Analytics for Risk Assessment -- Support for Healthcare Professionals -- NLP in Electronic Health Records -- Enhancing Remote Monitoring and Telemedicine -- Accelerating Drug Discovery -- Addressing Mental Health -- Conclusion and Future Scope -- References -- 14 AI-Assisted Diagnosis and Treatment Planning -- A Discussion of How AI Can Assist Healthcare Professionals in Making More Accurate Diagnos.