Название: AI in Disease Detection: Advancements and ApplicationsАвтор: Rajesh Singh, Anita Gehlot, Navjot Rathour, Shaik Vaseem Akram
Издательство: Wiley-IEEE Press
Год: 2025
Страниц: 395
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB
Comprehensive resource encompassing recent developments, current use cases, and future opportunities for Artificial Intelligence (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 (NLP), 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
The 15 chapters cover the following topics:
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.
2) Machine Learning Algorithms for Disease Detection – An explanation of the various machine learning algorithms used in disease detection, such as decision trees and neural networks.
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.
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.
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.
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15) AI in Disease Surveillance – An overview of how AI can be used in disease surveillance and outbreak detection, including case studies of its use in real-world scenarios.
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.
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