AI in Disease Detection - Advancements and Applications (Jan 29, 2025)_(1394278667)_(Wiley-IEEE Press)

Comprehensive resource encompassing recent developments, current use cases, and future opportunities of 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. 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 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 Chapter 10 Clinical Validation of AI Disease Detection Models — An Overview of the Clini