AI in Disease Detection : Advancements and Applications

• Autore: Singh Rajesh
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2025; ©2025
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (403 pages)
• Disciplina: 616.07/50285
• Soggetto topico: Diagnosis - Data processing; Artificial intelligence - Medical applications; Diagnosis - Technological innovations
• Altri autori: GehlotAnita RathourNavjot Vaseem AkramShaik
• Nota di contenuto: Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 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 -- Introduction -- Objectives -- Literature Review -- Benefits of AI in Disease Detection -- Limitations of AI in Disease Detection -- AI Techniques in Disease Detection -- Supervised Learning for Disease Diagnosis -- Unsupervised Learning in Healthcare -- Deep Learning and Convolutional Neural Networks (CNNs) -- AI in Medical Imaging and Radiology -- Applications of AI in Disease Detection -- Oncology: Cancer Detection and Diagnosis -- Cardiology: Predicting Cardiovascular Diseases -- Neurology: Early Detection of Neurological Disorders -- Infectious Diseases: AI in Epidemic and Pandemic Management -- Methodology -- Data Collection and Preprocessing -- Multimodal Fusion Techniques -- Transfer Learning for Disease Detection -- Explainable AI (XAI) Techniques -- Federated Learning Framework -- Clinical Validation and Adoption Studies -- Continuous Monitoring and Early Warning Systems -- Results and Analysis -- Analysis -- Performance Evaluation for the Techniques of Multimodal Fusion -- Assessment of Transfer Learning for Disease Detection -- Effectiveness of Explainable AI Techniques -- Privacy-Preserving Federated Learning-based Collaborative Model Training -- Performance of Continuous Monitoring and Early Warning Systems -- Case Study: AI in Disease Detection -- Development and Training -- Testing and Validation -- Deployment and Integration -- Conclusion -- Future Scope -- References -- Chapter 2 Explanation of Machine Learning Algorithms Used in Disease Detection, Such as Decision Trees and Neural Networks -- Introduction. ; The Silent Guardian: Machine Learning's Stealthy Rise in Disease Detection -- Beyond the Usual Suspects: A Look at Emerging Innovations -- The Ethical Symphony: Balancing Innovation with Human Oversight -- Objectives -- Unveiling Hidden Patterns - Feature Engineering -- Innovation Spotlight: Active Feature Acquisition (AFA) -- Limitations and Advantages of ML Algorithms for Disease Detection -- Advantages of Machine Learning Algorithms for Disease Detection -- Limitations of Machine Learning Algorithms for Disease Detection -- Literature Review -- The Familiar Melodies: Established ML Techniques and Their Strengths -- The Rise of the Deep Learning Chorus: Innovation on the Horizon -- Breaking New Ground: Unveiling Unique Innovations and Addressing Challenges -- The Well-Honed Orchestra: Established Techniques Take Center Stage -- Beyond the Familiar Melodies: Deep Learning Takes the Stage -- Collaboration and Innovation Lead the Way -- Methodology -- Conventional ML Methods for Disease Detection -- Beyond the Established Melodies: Innovation Takes Center Stage -- Results and Analysis -- The Familiar Melody: Established Methodologies -- The Disruptive Score: Unveiling New Innovations -- The Human Touch: Ethical Considerations and Explainability -- Conclusions and Future Scope -- The Evolving Maestro: AI Orchestration Beyond Established Methods -- Human-Machine Duet: Collaboration for a Healthier Future -- References -- Chapter 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 -- Introduction -- Objectives -- Early Infection Location through Phonetic Fingerprints -- Estimation Examination for All-encompassing Healthcare -- Social Media Reconnaissance for Disease Outbreaks. ; Custom-fitted Medication through Personalized Content Investigation -- Precise Medication with Clinical Trial Content Mining -- Breaking Down Language Boundaries for Worldwide Wellbeing -- Human-Machine Collaboration for Making Strides -- Advantages and Limitations of Natural Language Processing in Disease Detection -- Advantages of NLP in Disease Detection -- Limitations of NLP in Disease Detection -- Literature Review -- From Content to Determination: Revealing Etymological Fingerprints -- Past Watchwords: Capturing the Subtlety of Free-Text Information -- Control of Expansive Language Models: A New Frontier -- Breaking Down Language Obstructions for Worldwide -- Toward a Collaborative Future: Human-Machine Association -- Logical AI -- Past Content: Multimodal Infection Discovery with NLP and Imaging Information -- Methodology -- Information Procurement and Preprocessing: Building the Establishment -- Content Explanation: Labeling the Story -- Feature Designing: Extricating Important Signals -- Show Determination and Preparing: Choosing the Right Tool for the Work -- Demonstrate Assessment and Refinement: Guaranteeing Exactness and Belief -- Integration and Arrangement: Putting NLP to Work -- Results and Analysis -- Current Achievements: A Glimpse into the Possible -- Unveiling New Frontiers: Innovative Approaches for the Future -- Challenges and Considerations: Navigating the Road Ahead -- Case Study of NLP in Disease Detection -- Conclusions and Future Scope -- Charting the Course: Unveiling New Frontiers in NLP -- A Collaborative Future: Working Together for a Healthier Tomorrow -- Enhancing EHR Analysis -- Personalized Pharmaceutical -- Integration with AI and Machine Learning -- Expansion into New Medical Fields -- Upgrading Persistent Engagement -- Ethical and Protection Contemplations -- References. ; Chapter 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 -- Introduction -- Objectives -- Improved Early Disease Detection -- Improve Diagnostic Accuracy -- Developing Transfer Learning Models for Medical Imaging -- Explainability in Artificial Intelligence Applied to Medical Imaging -- Building Computer-Vision-Based Real-Time Disease Diagnostics Systems -- Integration of Multimodal Data for Comprehensive Diagnosis -- Literature Review -- Improving Early Detection and Diagnostic Accuracy -- Switch Studying and Artificial Records Generation -- Explainable AI and Real-Time Detection Structures -- Multimodal Statistics Integration -- Innovations in Precise Disease Detection -- Advanced Deep Learning Strategies -- Statistics Augmentation and Synthesis -- Explainable AI for Trust and Transparency -- Real-Time Diagnostic Systems -- Integration of Multimodal Insights -- Disease-specific Innovations -- Benefits of AI in Disease Detection -- Limitations of AI in Disease Detection -- Methodology -- Records Series and Preprocessing -- Version Improvement -- Real-Time Processing and Deployment -- Multimodal Records Integration -- Continuous Mastering and Development -- Results and Analysis -- Diagnostic Accuracy -- Efficiency and Pace -- Explainability and Agreement -- Multimodal Statistics Integration -- Key Improvements -- Continuous Learning and Variation -- Medical Integration and Impact -- Key Improvements -- Conclusion and Future Scope -- References -- Chapter 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 -- Introduction -- Objectives -- Literature Review -- Integration of Multimodal Information. ; Switch Learning for Better Model Training -- Explainable AI Techniques for CNNs -- Records Augmentation and Synthesis Techniques -- Fundamentals of Deep Learning -- CNNs in Medical Imaging -- Image Processing for Disease Detection -- Methodology -- Convolutional Neural Networks: A Top-level View -- Multiscale Convolutional Layers -- Attention Mechanisms -- Transfer Learning with Pretrained Models -- Generative Adversarial Networks (GANs) for Statistics Augmentation -- Self-Supervised Learning -- Results and Analysis -- Accuracy and Performance -- Enhanced Diagnostic Accuracy -- Sensitivity and Specificity -- Speed and Efficiency -- Reliability and Consistency -- Effects -- Multiscale Convolutional Layers -- Attention Mechanisms -- Switch Learning with Pretrained Models -- GANs for Statistics Augmentation -- Self-SupervisedLearning -- Improved Diagnostic Accuracy and Performance -- Reduced Dependence on Massive Labeled Datasets -- Better Version Robustness and Generalization -- Scalability and Flexibility -- Innovations and Future Instructions -- Multimodal Gaining Knowledge -- Federated Learning for Privateness-RetainingAI -- Explainable AI (XAI) for Stepped Forward Interpretability -- Integration with Wearable Devices -- Real-TimeAdaptive 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. ; Chapter 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.
• Sommario/riassunto: "A comprehensive resource encompassing recent developments and potential uses of AI in disease detection is in tremendous demand. Healthcare professionals, researchers, and students are turning more frequently to artificial intelligence (AI) to improve disease detection and diagnosis, but there is limited availability of readily available, latest resources that offer an in-depth examination of the field. AI in Disease Detection: Advancements and Applications fulfils the demand by offering a practical and broad guide to AI in disease detection. The book explores the fundamental concepts of AI and machine learning in the context of disease detection, the challenges and opportunities associated with using AI in healthcare, and the major applications of AI in disease detection. Healthcare experts, researchers, and students will gain practical skills in evaluating big data for health care, integrating artificial intelligence (AI) methods to medical data, and assessing the social and ethical consequences of AI in healthcare. They will also be able to recognise the essential AI applications in disease detection, enabling these individuals to make informed decisions when incorporating AI into their work or research. Overall, this book presents an overview of the transformation, progression, and applications of AI in disease detection. The book contains substantial findings and practical recommendations for researchers, healthcare professionals, and policymakers interested in studying the ability of AI to boost disease detection and patient outcomes"-- Provided by publisher.
• Titolo autorizzato: AI in Disease Detection
• ISBN: 9781394278695; 1394278691; 9781394278671; 1394278675; 9781394278688; 1394278683
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911018804203321
• Lo trovi qui: Univ. Federico II