{"product_id":"ai-in-disease-detection-advancements-and-applications-1st-edition","title":"AI in Disease Detection: Advancements and Applications 1st Edition","description":"\u003cdiv data-cel-widget=\"bookDescription_feature_div\" data-csa-c-id=\"7y9w7h-rgk3d4-2xixgp-6jetc1\" data-csa-c-is-in-initial-active-row=\"false\" data-csa-c-asin=\"0993745598\" data-csa-c-slot-id=\"bookDescription_feature_div\" data-csa-c-content-id=\"bookDescription\" data-csa-c-type=\"widget\" data-feature-name=\"bookDescription\" class=\"celwidget\" id=\"bookDescription_feature_div\"\u003e\n\u003cdiv class=\"a-expander-collapsed-height a-row a-expander-container a-spacing-base a-expander-partial-collapse-container\" data-a-expander-collapsed-height=\"280\" data-a-expander-name=\"book_description_expander\"\u003e\n\u003cdiv class=\"a-expander-content a-expander-partial-collapse-content\" aria-expanded=\"false\"\u003e\n\u003cdiv id=\"bookDescription_feature_div\" class=\"celwidget\" data-feature-name=\"bookDescription\" data-csa-c-type=\"widget\" data-csa-c-content-id=\"bookDescription\" data-csa-c-slot-id=\"bookDescription_feature_div\" data-csa-c-asin=\"\" data-csa-c-is-in-initial-active-row=\"false\" data-csa-c-id=\"a7ch8-ysp344-ep7bte-z37xip\" data-cel-widget=\"bookDescription_feature_div\"\u003e\n\u003cdiv data-a-expander-name=\"book_description_expander\" data-a-expander-collapsed-height=\"280\" class=\"a-expander-collapsed-height a-row a-expander-container a-spacing-base a-expander-partial-collapse-container\"\u003e\n\u003cdiv data-expanded=\"false\" class=\"a-expander-content a-expander-partial-collapse-content\"\u003e\n\u003cp\u003e\u003cspan class=\"a-text-bold\"\u003eComprehensive resource encompassing recent developments, current use cases, and future opportunities for AI in disease detection\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"a-text-italic\"\u003eAI in Disease Detection\u003c\/span\u003e\u003cspan\u003e 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThis 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eSample topics explored in \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eAI in Disease Detection\u003c\/span\u003e\u003cspan\u003e include:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLegal implication of AI in healthcare, with approaches to ensure privacy and security of patients and their data\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eIdentification of new biomarkers for disease detection, prediction of disease outcomes, and customized treatment plans depending on patient characteristics\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eAI’s role in disease surveillance and outbreak detection, with case studies of its current usage in real-world scenarios\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eClinical validation processes for AI disease detection models and how they can be validated for accuracy and effectiveness\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eDelivering excellent coverage of the subject, \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eAI in Disease Detection\u003c\/span\u003e\u003cspan\u003e 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv id=\"aboutauthors-section\" class=\"aboutauthors-section\"\u003e\n\u003cdiv class=\"page-section\"\u003e\n\u003cdiv data-toggle=\"collapse\" class=\"section-title collapsed\"\u003eAbout the Author\u003c\/div\u003e\n\u003cdiv class=\"section-content collapsed\"\u003e\n\u003cp\u003e\u003cb\u003eDr. Rajesh Singh,\u003c\/b\u003e\u003cspan\u003e \u003c\/span\u003eProfessor, Electronics \u0026amp; Communication Engineering and Director, Research \u0026amp; 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.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eDr. Anita Gehlot,\u003c\/b\u003e\u003cspan\u003e \u003c\/span\u003eProfessor, Electronics \u0026amp; Communication Engineering and Head -Research and Innovation, Uttaranchal University, India.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eDr. Navjot Rathour,\u003c\/b\u003e\u003cspan\u003e \u003c\/span\u003eAssociate Professor, Electronics \u0026amp; Communication Engineering, Chandigarh University, Mohali, India.\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eDr. Shaik Vaseem Akram,\u003c\/b\u003e\u003cspan\u003e \u003c\/span\u003eAssistant Professor, Electronics \u0026amp; Communication Engineering, S R University, Telangana, India.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv id=\"permissions-section\" class=\"permissions-section\"\u003e\n\u003cdiv class=\"page-section\"\u003e\n\u003cdiv data-toggle=\"collapse\" class=\"section-title collapsed\"\u003e \u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv id=\"tableofcontents-section\" class=\"tableofcontents-section\"\u003e\n\u003cdiv class=\"page-section\"\u003e\n\u003cdiv data-toggle=\"collapse\" class=\"section-title collapsed\"\u003eTable of Contents\u003c\/div\u003e\n\u003cdiv class=\"section-content collapsed\"\u003e\n\u003cp\u003eAbout the Editors xix\u003c\/p\u003e\n\u003cp\u003eList of Contributors xxi\u003c\/p\u003e\n\u003cp\u003ePreface xxiii\u003c\/p\u003e\n\u003cp\u003eAcknowledgments xxv\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e1 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eArvind Singh Rawat, Jagadheswaran Rajendran, and Shailendra Singh Sikarwar\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 1\u003c\/p\u003e\n\u003cp\u003eObjectives 2\u003c\/p\u003e\n\u003cp\u003eLiterature Review 4\u003c\/p\u003e\n\u003cp\u003eBenefits of AI in Disease Detection 7\u003c\/p\u003e\n\u003cp\u003eLimitations of AI in Disease Detection 9\u003c\/p\u003e\n\u003cp\u003eAI Techniques in Disease Detection 10\u003c\/p\u003e\n\u003cp\u003eSupervised Learning for Disease Diagnosis 10\u003c\/p\u003e\n\u003cp\u003eUnsupervised Learning in Healthcare 10\u003c\/p\u003e\n\u003cp\u003eDeep Learning and Convolutional Neural Networks (CNNs) 11\u003c\/p\u003e\n\u003cp\u003eAI in Medical Imaging and Radiology 11\u003c\/p\u003e\n\u003cp\u003eApplications of AI in Disease Detection 12\u003c\/p\u003e\n\u003cp\u003eOncology: Cancer Detection and Diagnosis 12\u003c\/p\u003e\n\u003cp\u003eCardiology: Predicting Cardiovascular Diseases 12\u003c\/p\u003e\n\u003cp\u003eNeurology: Early Detection of Neurological Disorders 12\u003c\/p\u003e\n\u003cp\u003eInfectious Diseases: AI in Epidemic and Pandemic Management 13\u003c\/p\u003e\n\u003cp\u003eMethodology 13\u003c\/p\u003e\n\u003cp\u003eData Collection and Preprocessing 13\u003c\/p\u003e\n\u003cp\u003eMultimodal Fusion Techniques 14\u003c\/p\u003e\n\u003cp\u003eTransfer Learning for Disease Detection 14\u003c\/p\u003e\n\u003cp\u003eExplainable AI (XAI) Techniques 14\u003c\/p\u003e\n\u003cp\u003eFederated Learning Framework 14\u003c\/p\u003e\n\u003cp\u003eClinical Validation and Adoption Studies 16\u003c\/p\u003e\n\u003cp\u003eContinuous Monitoring and Early Warning Systems 16\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 16\u003c\/p\u003e\n\u003cp\u003eAnalysis 17\u003c\/p\u003e\n\u003cp\u003ePerformance Evaluation for the Techniques of Multimodal Fusion 17\u003c\/p\u003e\n\u003cp\u003eAssessment of Transfer Learning for Disease Detection 18\u003c\/p\u003e\n\u003cp\u003eEffectiveness of Explainable AI Techniques 18\u003c\/p\u003e\n\u003cp\u003ePrivacy-Preserving Federated Learning-Based Collaborative Model Training 18\u003c\/p\u003e\n\u003cp\u003ePerformance of Continuous Monitoring and Early Warning Systems 19\u003c\/p\u003e\n\u003cp\u003eCase Study: AI in Disease Detection 20\u003c\/p\u003e\n\u003cp\u003eDevelopment and Training 20\u003c\/p\u003e\n\u003cp\u003eTesting and Validation 20\u003c\/p\u003e\n\u003cp\u003eDeployment and Integration 21\u003c\/p\u003e\n\u003cp\u003eConclusion 22\u003c\/p\u003e\n\u003cp\u003eFuture Scope 23\u003c\/p\u003e\n\u003cp\u003eReferences 24\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e2 Explanation of Machine Learning Algorithms Used in Disease Detection, Such as Decision Trees and Neural Networks 27\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNikhil Verma, Tripti Sharma, and Bobbinpreet Kaur\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 27\u003c\/p\u003e\n\u003cp\u003eThe Silent Guardian: Machine Learning’s Stealthy Rise in Disease Detection 27\u003c\/p\u003e\n\u003cp\u003eBeyond the Usual Suspects: A Look at Emerging Innovations 27\u003c\/p\u003e\n\u003cp\u003eThe Ethical Symphony: Balancing Innovation with Human Oversight 28\u003c\/p\u003e\n\u003cp\u003eObjectives 28\u003c\/p\u003e\n\u003cp\u003eUnveiling Hidden Patterns – Feature Engineering 28\u003c\/p\u003e\n\u003cp\u003eInnovation Spotlight: Active Feature Acquisition (AFA) 29\u003c\/p\u003e\n\u003cp\u003eLimitations and Advantages of ML Algorithms for Disease Detection 30\u003c\/p\u003e\n\u003cp\u003eAdvantages of Machine Learning Algorithms for Disease Detection 31\u003c\/p\u003e\n\u003cp\u003eLimitations of Machine Learning Algorithms for Disease Detection 31\u003c\/p\u003e\n\u003cp\u003eLiterature Review 32\u003c\/p\u003e\n\u003cp\u003eThe Familiar Melodies: Established ML Techniques and Their Strengths 33\u003c\/p\u003e\n\u003cp\u003eThe Rise of the Deep Learning Chorus: Innovation on the Horizon 33\u003c\/p\u003e\n\u003cp\u003eBreaking New Ground: Unveiling Unique Innovations and Addressing Challenges 38\u003c\/p\u003e\n\u003cp\u003eThe Well-Honed Orchestra: Established Techniques Take Center Stage 38\u003c\/p\u003e\n\u003cp\u003eBeyond the Familiar Melodies: Deep Learning Takes the Stage 39\u003c\/p\u003e\n\u003cp\u003eCollaboration and Innovation Lead the Way 40\u003c\/p\u003e\n\u003cp\u003eMethodology 40\u003c\/p\u003e\n\u003cp\u003eConventional ML Methods for Disease Detection 41\u003c\/p\u003e\n\u003cp\u003eBeyond the Established Melodies: Innovation Takes Center Stage 42\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 43\u003c\/p\u003e\n\u003cp\u003eThe Familiar Melody: Established Methodologies 43\u003c\/p\u003e\n\u003cp\u003eThe Disruptive Score: Unveiling New Innovations 44\u003c\/p\u003e\n\u003cp\u003eThe Human Touch: Ethical Considerations and Explainability 45\u003c\/p\u003e\n\u003cp\u003eConclusions and Future Scope 45\u003c\/p\u003e\n\u003cp\u003eThe Evolving Maestro: AI Orchestration Beyond Established Methods 46\u003c\/p\u003e\n\u003cp\u003eHuman-Machine Duet: Collaboration for a Healthier Future 46\u003c\/p\u003e\n\u003cp\u003eReferences 47\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e3 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVinod Kumar, Mohammed Ismail Iqbal, and Rachna Rathore\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 53\u003c\/p\u003e\n\u003cp\u003eObjectives 54\u003c\/p\u003e\n\u003cp\u003eEarly Infection Location through Phonetic Fingerprints 54\u003c\/p\u003e\n\u003cp\u003eEstimation Examination for All-Encompassing Healthcare 55\u003c\/p\u003e\n\u003cp\u003eSocial Media Reconnaissance for Disease Outbreaks 55\u003c\/p\u003e\n\u003cp\u003eCustom-Fitted Medication through Personalized Content Investigation 55\u003c\/p\u003e\n\u003cp\u003ePrecise Medication with Clinical Trial Content Mining 56\u003c\/p\u003e\n\u003cp\u003eBreaking Down Language Boundaries for Worldwide Wellbeing 56\u003c\/p\u003e\n\u003cp\u003eHuman-Machine Collaboration for Making Strides 56\u003c\/p\u003e\n\u003cp\u003eAdvantages and Limitations of Natural Language Processing in Disease Detection 57\u003c\/p\u003e\n\u003cp\u003eAdvantages of NLP in Disease Detection 57\u003c\/p\u003e\n\u003cp\u003eLimitations of NLP in Disease Detection 58\u003c\/p\u003e\n\u003cp\u003eLiterature Review 59\u003c\/p\u003e\n\u003cp\u003eFrom Content to Determination: Revealing Etymological Fingerprints 59\u003c\/p\u003e\n\u003cp\u003ePast Watchwords: Capturing the Subtlety of Free-Text Information 59\u003c\/p\u003e\n\u003cp\u003eControl of Expansive Language Models: A New Frontier 59\u003c\/p\u003e\n\u003cp\u003eBreaking Down Language Obstructions for Worldwide 61\u003c\/p\u003e\n\u003cp\u003eToward a Collaborative Future: Human-Machine Association 61\u003c\/p\u003e\n\u003cp\u003eLogical AI 61\u003c\/p\u003e\n\u003cp\u003ePast Content: Multimodal Infection Discovery with NLP and Imaging Information 62\u003c\/p\u003e\n\u003cp\u003eMethodology 62\u003c\/p\u003e\n\u003cp\u003eInformation Procurement and Preprocessing: Building the Establishment 62\u003c\/p\u003e\n\u003cp\u003eContent Explanation: Labeling the Story 63\u003c\/p\u003e\n\u003cp\u003eFeature Designing: Extricating Important Signals 63\u003c\/p\u003e\n\u003cp\u003eShow Determination and Preparing: Choosing the Right Tool for the Work 63\u003c\/p\u003e\n\u003cp\u003eDemonstrate Assessment and Refinement: Guaranteeing Exactness and Belief 63\u003c\/p\u003e\n\u003cp\u003eIntegration and Arrangement: Putting NLP to Work 64\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 64\u003c\/p\u003e\n\u003cp\u003eCurrent Achievements: A Glimpse into the Possible 64\u003c\/p\u003e\n\u003cp\u003eUnveiling New Frontiers: Innovative Approaches for the Future 66\u003c\/p\u003e\n\u003cp\u003eChallenges and Considerations: Navigating the Road Ahead 66\u003c\/p\u003e\n\u003cp\u003eCase Study of NLP in Disease Detection 67\u003c\/p\u003e\n\u003cp\u003eConclusions and Future Scope 69\u003c\/p\u003e\n\u003cp\u003eCharting the Course: Unveiling New Frontiers in NLP 70\u003c\/p\u003e\n\u003cp\u003eA Collaborative Future: Working Together for a Healthier Tomorrow 70\u003c\/p\u003e\n\u003cp\u003eEnhancing EHR Analysis 71\u003c\/p\u003e\n\u003cp\u003ePersonalized Pharmaceutical 71\u003c\/p\u003e\n\u003cp\u003eIntegration with AI and Machine Learning 72\u003c\/p\u003e\n\u003cp\u003eExpansion into New Medical Fields 72\u003c\/p\u003e\n\u003cp\u003eUpgrading Persistent Engagement 72\u003c\/p\u003e\n\u003cp\u003eEthical and Protection Contemplations 73\u003c\/p\u003e\n\u003cp\u003eReferences 73\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e4 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRavindra Sharma, Narendra Kumar, and Vinod Sharma\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 77\u003c\/p\u003e\n\u003cp\u003eObjectives 78\u003c\/p\u003e\n\u003cp\u003eImproved Early Disease Detection 78\u003c\/p\u003e\n\u003cp\u003eImprove Diagnostic Accuracy 78\u003c\/p\u003e\n\u003cp\u003eDeveloping Transfer Learning Models for Medical Imaging 78\u003c\/p\u003e\n\u003cp\u003eExplainability in Artificial Intelligence Applied to Medical Imaging 79\u003c\/p\u003e\n\u003cp\u003eBuilding Computer-Vision-Based Real-Time Disease Diagnostics Systems 79\u003c\/p\u003e\n\u003cp\u003eIntegration of Multimodal Data for Comprehensive Diagnosis 79\u003c\/p\u003e\n\u003cp\u003eLiterature Review 79\u003c\/p\u003e\n\u003cp\u003eImproving Early Detection and Diagnostic Accuracy 80\u003c\/p\u003e\n\u003cp\u003eSwitch Studying and Artificial Records Generation 80\u003c\/p\u003e\n\u003cp\u003eExplainable AI and Real-Time Detection Structures 80\u003c\/p\u003e\n\u003cp\u003eMultimodal Statistics Integration 81\u003c\/p\u003e\n\u003cp\u003eInnovations in Precise Disease Detection 81\u003c\/p\u003e\n\u003cp\u003eAdvanced Deep Learning Strategies 83\u003c\/p\u003e\n\u003cp\u003eStatistics Augmentation and Synthesis 83\u003c\/p\u003e\n\u003cp\u003eExplainable AI for Trust and Transparency 83\u003c\/p\u003e\n\u003cp\u003eReal-Time Diagnostic Systems 84\u003c\/p\u003e\n\u003cp\u003eIntegration of Multimodal Insights 84\u003c\/p\u003e\n\u003cp\u003eDisease-Specific Innovations 84\u003c\/p\u003e\n\u003cp\u003eBenefits of AI in Disease Detection 85\u003c\/p\u003e\n\u003cp\u003eLimitations of AI in Disease Detection 86\u003c\/p\u003e\n\u003cp\u003eMethodology 87\u003c\/p\u003e\n\u003cp\u003eRecords Series and Preprocessing 87\u003c\/p\u003e\n\u003cp\u003eVersion Improvement 88\u003c\/p\u003e\n\u003cp\u003eReal-Time Processing and Deployment 88\u003c\/p\u003e\n\u003cp\u003eMultimodal Records Integration 89\u003c\/p\u003e\n\u003cp\u003eContinuous Mastering and Development 89\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 89\u003c\/p\u003e\n\u003cp\u003eDiagnostic Accuracy 91\u003c\/p\u003e\n\u003cp\u003eEfficiency and Pace 91\u003c\/p\u003e\n\u003cp\u003eExplainability and Agreement 92\u003c\/p\u003e\n\u003cp\u003eMultimodal Statistics Integration 92\u003c\/p\u003e\n\u003cp\u003eKey Improvements 92\u003c\/p\u003e\n\u003cp\u003eContinuous Learning and Variation 93\u003c\/p\u003e\n\u003cp\u003eMedical Integration and Impact 93\u003c\/p\u003e\n\u003cp\u003eKey Improvements 93\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 94\u003c\/p\u003e\n\u003cp\u003eReferences 96\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e5 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMohammed Ismail Iqbal and Priyanka Kaushik\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 99\u003c\/p\u003e\n\u003cp\u003eObjectives 100\u003c\/p\u003e\n\u003cp\u003eLiterature Review 101\u003c\/p\u003e\n\u003cp\u003eIntegration of Multimodal Information 102\u003c\/p\u003e\n\u003cp\u003eSwitch Learning for Better Model Training 102\u003c\/p\u003e\n\u003cp\u003eExplainable AI Techniques for CNNs 102\u003c\/p\u003e\n\u003cp\u003eRecords Augmentation and Synthesis Techniques 103\u003c\/p\u003e\n\u003cp\u003eFundamentals of Deep Learning 105\u003c\/p\u003e\n\u003cp\u003eCNNs in Medical Imaging 106\u003c\/p\u003e\n\u003cp\u003eImage Processing for Disease Detection 107\u003c\/p\u003e\n\u003cp\u003eMethodology 109\u003c\/p\u003e\n\u003cp\u003eConvolutional Neural Networks: A Top-Level View 109\u003c\/p\u003e\n\u003cp\u003eMultiscale Convolutional Layers 109\u003c\/p\u003e\n\u003cp\u003eAttention Mechanisms 109\u003c\/p\u003e\n\u003cp\u003eTransfer Learning with Pretrained Models 110\u003c\/p\u003e\n\u003cp\u003eGenerative Adversarial Networks (GANs) for Statistics Augmentation 110\u003c\/p\u003e\n\u003cp\u003eSelf-Supervised Learning 110\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 111\u003c\/p\u003e\n\u003cp\u003eAccuracy and Performance 112\u003c\/p\u003e\n\u003cp\u003eEnhanced Diagnostic Accuracy 112\u003c\/p\u003e\n\u003cp\u003eSensitivity and Specificity 113\u003c\/p\u003e\n\u003cp\u003eSpeed and Efficiency 113\u003c\/p\u003e\n\u003cp\u003eReliability and Consistency 113\u003c\/p\u003e\n\u003cp\u003eEffects 114\u003c\/p\u003e\n\u003cp\u003eMultiscale Convolutional Layers 114\u003c\/p\u003e\n\u003cp\u003eAttention Mechanisms 115\u003c\/p\u003e\n\u003cp\u003eSwitch Learning with Pretrained Models 115\u003c\/p\u003e\n\u003cp\u003eGANs for Statistics Augmentation 115\u003c\/p\u003e\n\u003cp\u003eSelf-Supervised Learning 115\u003c\/p\u003e\n\u003cp\u003eImproved Diagnostic Accuracy and Performance 115\u003c\/p\u003e\n\u003cp\u003eReduced Dependence on Massive Labeled Datasets 116\u003c\/p\u003e\n\u003cp\u003eBetter Version Robustness and Generalization 116\u003c\/p\u003e\n\u003cp\u003eScalability and Flexibility 116\u003c\/p\u003e\n\u003cp\u003eInnovations and Future Instructions 116\u003c\/p\u003e\n\u003cp\u003eMultimodal Gaining Knowledge 116\u003c\/p\u003e\n\u003cp\u003eFederated Learning for Privateness-Retaining AI 116\u003c\/p\u003e\n\u003cp\u003eExplainable AI (XAI) for Stepped Forward Interpretability 116\u003c\/p\u003e\n\u003cp\u003eIntegration with Wearable Devices 117\u003c\/p\u003e\n\u003cp\u003eReal-Time Adaptive Learning 117\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 117\u003c\/p\u003e\n\u003cp\u003eMultimodal Deep Learning Integration 118\u003c\/p\u003e\n\u003cp\u003eFederated Learning for Stronger Privacy 118\u003c\/p\u003e\n\u003cp\u003eExplainable AI (XAI) for Transparency 118\u003c\/p\u003e\n\u003cp\u003eWearable Generation AI and Continuous Monitoring 119\u003c\/p\u003e\n\u003cp\u003eAdaptive Learning and Real-Time Model Updating 119\u003c\/p\u003e\n\u003cp\u003ePersonalized Remedy and Predictive Analytics 119\u003c\/p\u003e\n\u003cp\u003eCollaborative AI Systems 119\u003c\/p\u003e\n\u003cp\u003eStronger Data Augmentation Techniques 119\u003c\/p\u003e\n\u003cp\u003eAI-Driven Clinical Trials and Research 120\u003c\/p\u003e\n\u003cp\u003eInternational Health and AI-Driven Disorder Surveillance 120\u003c\/p\u003e\n\u003cp\u003eReferences 120\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e6 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSatish Mahadevan Srinivasan and Vinod Sharma\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 123\u003c\/p\u003e\n\u003cp\u003eObjectives 124\u003c\/p\u003e\n\u003cp\u003eLiterature Review 126\u003c\/p\u003e\n\u003cp\u003eFundamentals of AI in Medical Applications 129\u003c\/p\u003e\n\u003cp\u003eMachine Learning vs. Deep Learning 129\u003c\/p\u003e\n\u003cp\u003eAI Techniques for Cardiovascular Disease Detection 131\u003c\/p\u003e\n\u003cp\u003eConvolutional Neural Networks (CNNs) 131\u003c\/p\u003e\n\u003cp\u003eRecurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks 131\u003c\/p\u003e\n\u003cp\u003eSupport Vector Machines (SVMs) 132\u003c\/p\u003e\n\u003cp\u003eRandom Forests 132\u003c\/p\u003e\n\u003cp\u003eAI in Cardiovascular Imaging 132\u003c\/p\u003e\n\u003cp\u003eAI in Echocardiography 133\u003c\/p\u003e\n\u003cp\u003eAI in Cardiac MRI and CT Scans 133\u003c\/p\u003e\n\u003cp\u003eAI in Nuclear Cardiology 133\u003c\/p\u003e\n\u003cp\u003eAI in Electrocardiogram (ECG) Analysis 134\u003c\/p\u003e\n\u003cp\u003eComputer-Based ECG Interpretation 134\u003c\/p\u003e\n\u003cp\u003eCase Studies and Real-World Implementations 134\u003c\/p\u003e\n\u003cp\u003eAI in Risk Prediction and Stratification 135\u003c\/p\u003e\n\u003cp\u003eRisk Prediction Models 135\u003c\/p\u003e\n\u003cp\u003ePersonalized Risk Stratification 136\u003c\/p\u003e\n\u003cp\u003eAI in Monitoring and Managing Cardiovascular Health 136\u003c\/p\u003e\n\u003cp\u003eAI-Assisted Disease Management 137\u003c\/p\u003e\n\u003cp\u003eChallenges and Limitations of AI in Cardiovascular Disease Detection 137\u003c\/p\u003e\n\u003cp\u003eData Quality and Availability 137\u003c\/p\u003e\n\u003cp\u003eModel Interpretability and Transparency 138\u003c\/p\u003e\n\u003cp\u003eClinical Integration and Adoption 138\u003c\/p\u003e\n\u003cp\u003eEthical and Legal Considerations 138\u003c\/p\u003e\n\u003cp\u003eMethodology 139\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 140\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 142\u003c\/p\u003e\n\u003cp\u003eReferences 144\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e7 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShival Dubey and Shailendra Singh Sikarwar\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 147\u003c\/p\u003e\n\u003cp\u003eObjectives 148\u003c\/p\u003e\n\u003cp\u003eLiterature Review 150\u003c\/p\u003e\n\u003cp\u003eMethodology 159\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 160\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 162\u003c\/p\u003e\n\u003cp\u003eReferences 163\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e8 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDolly Sharma and Priyanka Kaushik\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 167\u003c\/p\u003e\n\u003cp\u003eObjectives 168\u003c\/p\u003e\n\u003cp\u003eLiterature Review 169\u003c\/p\u003e\n\u003cp\u003eKey Applications of AI in Medical Settings 180\u003c\/p\u003e\n\u003cp\u003eAI Techniques for Detecting Alzheimer’s Disease 181\u003c\/p\u003e\n\u003cp\u003eAI Techniques for Detecting Parkinson’s Disease 181\u003c\/p\u003e\n\u003cp\u003eAI Techniques in Other Neurological Disorders 182\u003c\/p\u003e\n\u003cp\u003eMethodology 183\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 184\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 186\u003c\/p\u003e\n\u003cp\u003eReferences 187\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e9 AI Integration in Healthcare Systems — A Review of the Problems and Potential Associated with Integrating AI in Healthcare for Disease Detection and Diagnosis 191\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePraveen Kumar Malik, Hitesh Bhatt, and Madhuri Sharma\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 191\u003c\/p\u003e\n\u003cp\u003eObjectives 192\u003c\/p\u003e\n\u003cp\u003eLiterature Review 194\u003c\/p\u003e\n\u003cp\u003eAdvantages of AI Integration in Healthcare Systems for Disease Detection and Diagnosis 197\u003c\/p\u003e\n\u003cp\u003eLimitations of AI Integration in Healthcare Systems for Disease Detection and Diagnosis 199\u003c\/p\u003e\n\u003cp\u003eApplications of AI Integration in Healthcare Systems for Disease Detection and Diagnosis 200\u003c\/p\u003e\n\u003cp\u003eMethodology 203\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 205\u003c\/p\u003e\n\u003cp\u003eMore Desirable Diagnostic Accuracy and Efficiency 205\u003c\/p\u003e\n\u003cp\u003eInterpretability and Trustworthiness 205\u003c\/p\u003e\n\u003cp\u003eRobustness and Generalizability 207\u003c\/p\u003e\n\u003cp\u003eContinuous Learning and Version 207\u003c\/p\u003e\n\u003cp\u003ePatient Consequences and Healthcare Impact 207\u003c\/p\u003e\n\u003cp\u003eObservations 208\u003c\/p\u003e\n\u003cp\u003ePotential Benefits of AI Integration 208\u003c\/p\u003e\n\u003cp\u003eFuture Directions 209\u003c\/p\u003e\n\u003cp\u003eConclusion 209\u003c\/p\u003e\n\u003cp\u003eFuture Scope 210\u003c\/p\u003e\n\u003cp\u003eReferences 212\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e10 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eManish Prateek and Saurabh Pratap Singh Rathore\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 215\u003c\/p\u003e\n\u003cp\u003eObjectives 217\u003c\/p\u003e\n\u003cp\u003eLiterature Review 219\u003c\/p\u003e\n\u003cp\u003eAdvantages of the Clinical Validation of AI Disease Detection Models 223\u003c\/p\u003e\n\u003cp\u003eThe Clinical Validation Process 223\u003c\/p\u003e\n\u003cp\u003eClinical Trials 223\u003c\/p\u003e\n\u003cp\u003eLimitations of the Clinical Validation Process 224\u003c\/p\u003e\n\u003cp\u003eData Quality and Availability 224\u003c\/p\u003e\n\u003cp\u003eModel Generalizability 225\u003c\/p\u003e\n\u003cp\u003eRegulatory and Ethical Challenges 225\u003c\/p\u003e\n\u003cp\u003eIntegration with Clinical Workflow 225\u003c\/p\u003e\n\u003cp\u003eCost and Resource Requirements 225\u003c\/p\u003e\n\u003cp\u003eInterpretability and Transparency 225\u003c\/p\u003e\n\u003cp\u003eClinical Trial Limitations Narrow Focus 225\u003c\/p\u003e\n\u003cp\u003eApplications of AI Disease Detection Models 226\u003c\/p\u003e\n\u003cp\u003eRadiology and Medical Imaging 226\u003c\/p\u003e\n\u003cp\u003ePathology 226\u003c\/p\u003e\n\u003cp\u003eCardiology 226\u003c\/p\u003e\n\u003cp\u003eOphthalmology 228\u003c\/p\u003e\n\u003cp\u003eOncology 228\u003c\/p\u003e\n\u003cp\u003eNeurology 228\u003c\/p\u003e\n\u003cp\u003ePrimary Care 228\u003c\/p\u003e\n\u003cp\u003ePublic Health 228\u003c\/p\u003e\n\u003cp\u003eResearch and Development 229\u003c\/p\u003e\n\u003cp\u003eMethodology 229\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 230\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 233\u003c\/p\u003e\n\u003cp\u003eReferences 235\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e11 Integration of AI in Healthcare Systems — A Discussion of the Challenges and Opportunities of\u003c\/b\u003e\u003cspan\u003e \u003c\/span\u003e\u003cb\u003eIntegrating AI in Healthcare Systems for\u003c\/b\u003e\u003cb\u003e\u003cspan\u003e \u003c\/span\u003eDisease Detection and Diagnosis 239\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNitin Sharma and Priyanka Kaushik\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 239\u003c\/p\u003e\n\u003cp\u003eObjectives 240\u003c\/p\u003e\n\u003cp\u003eLiterature Review 242\u003c\/p\u003e\n\u003cp\u003eAdvantages of AI Integration in Healthcare Systems 245\u003c\/p\u003e\n\u003cp\u003eEnhanced Diagnostic Accuracy 245\u003c\/p\u003e\n\u003cp\u003eEarly Disease Detection 245\u003c\/p\u003e\n\u003cp\u003eContinuous Learning and Improvement 246\u003c\/p\u003e\n\u003cp\u003eLimitations and Challenges of Integrating AI in Healthcare Systems 247\u003c\/p\u003e\n\u003cp\u003eApplications of AI in Healthcare for Disease Detection and Diagnosis 250\u003c\/p\u003e\n\u003cp\u003eMedical Imaging Analysis 250\u003c\/p\u003e\n\u003cp\u003ePathology: 4,444 AI Systems Checking Biopsy Samples for Cancer Cells 250\u003c\/p\u003e\n\u003cp\u003eChronic Disease Management 252\u003c\/p\u003e\n\u003cp\u003eMethodology 252\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 253\u003c\/p\u003e\n\u003cp\u003eMore Desirable Diagnostic Accuracy and Efficiency 253\u003c\/p\u003e\n\u003cp\u003eInterpretability and Trustworthiness 254\u003c\/p\u003e\n\u003cp\u003ePatient Outcomes and Healthcare Impact 256\u003c\/p\u003e\n\u003cp\u003eObservations 256\u003c\/p\u003e\n\u003cp\u003eConclusion 259\u003c\/p\u003e\n\u003cp\u003eFuture Scope 259\u003c\/p\u003e\n\u003cp\u003eGrowth into Multi-Omics Records Integration 259\u003c\/p\u003e\n\u003cp\u003eDevelopment of AI-Driven Predictive Analytics for Physical Fitness 260\u003c\/p\u003e\n\u003cp\u003eEnhancement of Real-Time Data Selection Guide Structures 260\u003c\/p\u003e\n\u003cp\u003eImplementation of AI in Virtual and Telehealth Services 260\u003c\/p\u003e\n\u003cp\u003eEthical AI and Bias Mitigation Strategies 260\u003c\/p\u003e\n\u003cp\u003eCollaborative AI for Interdisciplinary Studies 260\u003c\/p\u003e\n\u003cp\u003ePersonalized Fitness Training and Lifestyle Interventions 261\u003c\/p\u003e\n\u003cp\u003eAugmented Reality (AR) and AI for Better Clinical Training 261\u003c\/p\u003e\n\u003cp\u003eReferences 261\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e12 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBinboga Siddik Yarman and Saurabh Pratap Singh Rathore\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 265\u003c\/p\u003e\n\u003cp\u003eObjectives 266\u003c\/p\u003e\n\u003cp\u003eLiterature Review 268\u003c\/p\u003e\n\u003cp\u003eAdvantages of AI in Disease Detection 271\u003c\/p\u003e\n\u003cp\u003eLimitations of AI in Disease Detection 273\u003c\/p\u003e\n\u003cp\u003eApplications of AI in Disease Detection 275\u003c\/p\u003e\n\u003cp\u003eMethodology 277\u003c\/p\u003e\n\u003cp\u003eResult and Analysis 280\u003c\/p\u003e\n\u003cp\u003eObservations 283\u003c\/p\u003e\n\u003cp\u003eUpgraded Diagnosis Accuracy 283\u003c\/p\u003e\n\u003cp\u003eMoving Toward Personalized Treatment 283\u003c\/p\u003e\n\u003cp\u003eAdvances in Foundation Imaging 284\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 285\u003c\/p\u003e\n\u003cp\u003eReferences 286\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e13 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnchit Bijalwan and Shailendra Singh Sikarwar\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 289\u003c\/p\u003e\n\u003cp\u003eObjectives 290\u003c\/p\u003e\n\u003cp\u003eLiterature Review 292\u003c\/p\u003e\n\u003cp\u003eAdvantages of AI in Disease Detection: A Comprehensive Overview 295\u003c\/p\u003e\n\u003cp\u003eEnhanced Accuracy and Precision 295\u003c\/p\u003e\n\u003cp\u003eSpeedier Preparing and Determination 295\u003c\/p\u003e\n\u003cp\u003eTaking Care of Expansive Volumes of Information 295\u003c\/p\u003e\n\u003cp\u003eCeaseless Learning and Enhancement 296\u003c\/p\u003e\n\u003cp\u003eDiminishment of Human Mistake 296\u003c\/p\u003e\n\u003cp\u003eLimitations and Challenges of AI in Disease Detection 297\u003c\/p\u003e\n\u003cp\u003eApplications of AI in Disease Detection: A Comprehensive Overview 299\u003c\/p\u003e\n\u003cp\u003eMedical Imaging Analysis 299\u003c\/p\u003e\n\u003cp\u003eDrug Discovery and Development 300\u003c\/p\u003e\n\u003cp\u003eMethodology 302\u003c\/p\u003e\n\u003cp\u003eResult and Analysis 303\u003c\/p\u003e\n\u003cp\u003eObservations 306\u003c\/p\u003e\n\u003cp\u003eSignificant Impact on Medical Imaging 306\u003c\/p\u003e\n\u003cp\u003eAutomation and Efficiency in Pathology 306\u003c\/p\u003e\n\u003cp\u003eAdvancements in Genomics and Personalized Medicine 306\u003c\/p\u003e\n\u003cp\u003eEarly Detection and Proactive Health Management 306\u003c\/p\u003e\n\u003cp\u003ePredictive Analytics for Risk Assessment 307\u003c\/p\u003e\n\u003cp\u003eSupport for Healthcare Professionals 307\u003c\/p\u003e\n\u003cp\u003eNLP in Electronic Health Records 307\u003c\/p\u003e\n\u003cp\u003eEnhancing Remote Monitoring and Telemedicine 307\u003c\/p\u003e\n\u003cp\u003eAccelerating Drug Discovery 307\u003c\/p\u003e\n\u003cp\u003eAddressing Mental Health 308\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 308\u003c\/p\u003e\n\u003cp\u003eReferences 309\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e14 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMamoon Rashid and Madhuri Sharma\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 313\u003c\/p\u003e\n\u003cp\u003eObjectives 315\u003c\/p\u003e\n\u003cp\u003eLiterature Review 316\u003c\/p\u003e\n\u003cp\u003eAdvantages of AI-Assisted Diagnosis and Treatment Planning 319\u003c\/p\u003e\n\u003cp\u003eAdvanced Diagnostic Accuracy 319\u003c\/p\u003e\n\u003cp\u003ePersonalized Treatment Plans 320\u003c\/p\u003e\n\u003cp\u003eEfficient Data Management 320\u003c\/p\u003e\n\u003cp\u003eContinuous Learning and Improvement 320\u003c\/p\u003e\n\u003cp\u003ePredictive Analytics 320\u003c\/p\u003e\n\u003cp\u003eEfficient Workflow 320\u003c\/p\u003e\n\u003cp\u003eSupport for Rural and Underserved Areas 321\u003c\/p\u003e\n\u003cp\u003eLimitations of AI-Assisted Diagnosis and Treatment Planning 321\u003c\/p\u003e\n\u003cp\u003eConcerns with Data Privacy and Security 321\u003c\/p\u003e\n\u003cp\u003eData Quality and Bias 321\u003c\/p\u003e\n\u003cp\u003eLack of Interpretability 322\u003c\/p\u003e\n\u003cp\u003eGood-Quality Data 322\u003c\/p\u003e\n\u003cp\u003eIntegration with Existing Systems 322\u003c\/p\u003e\n\u003cp\u003eEthical and Legal Issues 322\u003c\/p\u003e\n\u003cp\u003eResistance to Change 323\u003c\/p\u003e\n\u003cp\u003eLimited Clinical Validation 323\u003c\/p\u003e\n\u003cp\u003eSummary of Challenges 323\u003c\/p\u003e\n\u003cp\u003eApplications of AI-Assisted Diagnosis and Treatment Planning 323\u003c\/p\u003e\n\u003cp\u003eTherapeutic Imaging Examination 325\u003c\/p\u003e\n\u003cp\u003ePersonalized Medicine 325\u003c\/p\u003e\n\u003cp\u003ePredictive Analytics for Disease Prevention 325\u003c\/p\u003e\n\u003cp\u003eDiscovery and Development of New Drugs 326\u003c\/p\u003e\n\u003cp\u003eVirtual Health Assistants 326\u003c\/p\u003e\n\u003cp\u003eRobotic Surgery 326\u003c\/p\u003e\n\u003cp\u003eClinical Decision Support Systems (CDSS) 326\u003c\/p\u003e\n\u003cp\u003eRemote Monitoring and Telemedicine 327\u003c\/p\u003e\n\u003cp\u003eOptimizing Workflows 327\u003c\/p\u003e\n\u003cp\u003eMethodology 327\u003c\/p\u003e\n\u003cp\u003eObservations 328\u003c\/p\u003e\n\u003cp\u003eResults and Analysis 331\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 333\u003c\/p\u003e\n\u003cp\u003eReferences 334\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e15 AI in Disease Surveillance — An Overview of How AI Can Be Used in Disease Surveillance and Outbreak Detection in Real-World Scenarios 337\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAbhishek Tripathi and Rachna Rathore\u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eIntroduction 337\u003c\/p\u003e\n\u003cp\u003eObjectives 338\u003c\/p\u003e\n\u003cp\u003eLiterature Review 340\u003c\/p\u003e\n\u003cp\u003eAdvantages of AI in Disease Surveillance 343\u003c\/p\u003e\n\u003cp\u003eLimitations of AI in Disease Surveillance 345\u003c\/p\u003e\n\u003cp\u003eInformation Quality and Accessibility 345\u003c\/p\u003e\n\u003cp\u003eProtection and Security Concerns 345\u003c\/p\u003e\n\u003cp\u003eInclination in AI Calculations 345\u003c\/p\u003e\n\u003cp\u003eInterpretability and Straightforwardness 345\u003c\/p\u003e\n\u003cp\u003eEthical and Legitimate Issues 345\u003c\/p\u003e\n\u003cp\u003eFoundation and Asset Imperatives 346\u003c\/p\u003e\n\u003cp\u003eVersatility to Advancing Dangers 346\u003c\/p\u003e\n\u003cp\u003eUntrue Positives and Negatives 346\u003c\/p\u003e\n\u003cp\u003eReal-World Case Thinks About Highlighting Confinements Google Flu Patterns (GFT) 346\u003c\/p\u003e\n\u003cp\u003eChallenges in Low-Resource Settings 346\u003c\/p\u003e\n\u003cp\u003eInclination in Predictive Models 347\u003c\/p\u003e\n\u003cp\u003eApplications of AI in Disease Surveillance 347\u003c\/p\u003e\n\u003cp\u003eEarly Detection Systems 347\u003c\/p\u003e\n\u003cp\u003ePredictive Modeling 347\u003c\/p\u003e\n\u003cp\u003eComputerized Information Collection and Integration 349\u003c\/p\u003e\n\u003cp\u003eReal-Time Reconnaissance 349\u003c\/p\u003e\n\u003cp\u003eNatural Language Programming (NLP) 349\u003c\/p\u003e\n\u003cp\u003eGeospatial Investigation 349\u003c\/p\u003e\n\u003cp\u003eContact Tracking 349\u003c\/p\u003e\n\u003cp\u003eSocial Media Investigation 349\u003c\/p\u003e\n\u003cp\u003eMethodology 350\u003c\/p\u003e\n\u003cp\u003eResult and Analysis 351\u003c\/p\u003e\n\u003cp\u003eObservations 354\u003c\/p\u003e\n\u003cp\u003eComprehensive Experiences 354\u003c\/p\u003e\n\u003cp\u003eKey Perceptions Upgraded Early Discovery 354\u003c\/p\u003e\n\u003cp\u003ePrecise Predictive Modeling 354\u003c\/p\u003e\n\u003cp\u003eReal-Time Checking 355\u003c\/p\u003e\n\u003cp\u003eNLP Capabilities 355\u003c\/p\u003e\n\u003cp\u003eGeospatial Examination and Mapping 355\u003c\/p\u003e\n\u003cp\u003eImproved Contact Tracking 355\u003c\/p\u003e\n\u003cp\u003eOpinion and Behavioral Examination 355\u003c\/p\u003e\n\u003cp\u003eChallenges and Considerations 356\u003c\/p\u003e\n\u003cp\u003eData Quality and Availability 356\u003c\/p\u003e\n\u003cp\u003eProtection and Ethical Concerns 356\u003c\/p\u003e\n\u003cp\u003ePredisposition in AI Models 356\u003c\/p\u003e\n\u003cp\u003eInterpretability and Straightforwardness 356\u003c\/p\u003e\n\u003cp\u003eFoundation and Asset Imperatives 356\u003c\/p\u003e\n\u003cp\u003eConclusion and Future Scope 357\u003c\/p\u003e\n\u003cp\u003eReferences 358\u003c\/p\u003e\n\u003cp\u003eIndex 361\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cb\u003eBOOKREAD™ 5-STEP SATISFACTION GUARANTEE\u003c\/b\u003e\u003c\/strong\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003e1. No Risk, 30-Day Money-Back Guarantee. \u003cbr\u003e2. instant download. No surprises or hidden fees.\u003cbr\u003e3. Safe Payments via Credit\/Debit Card or PayPal® \u003cbr\u003e4. McAfee™ and SSL secured shopping cart.\u003cbr\u003e5. lifetime customer support.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"My Store","offers":[{"title":"PDF","offer_id":56778125508939,"sku":null,"price":29.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1031\/1204\/8971\/files\/81yUOxVcT8L._SL1500.jpg?v=1773316499","url":"https:\/\/bookread.io\/products\/ai-in-disease-detection-advancements-and-applications-1st-edition","provider":"bookread","version":"1.0","type":"link"}