دانلود کتاب هوش مصنوعی و یادگیری ماشین برای مراقبت‌های بهداشتی – جلد 1 – تجزیه و تحلیل تصویر و داده

Advances in digital and computing technologies have driven a rapid growth in artificial intelligence (AI) and machine learning (ML) and their applications to a variety of sectors, including themedical and healthcare domains.Nowadays, electronicmedical records and diagnostic imaging of patients can be readily collected and analysed by utilising AI and ML systems to derive insights and assist medical professionals in making accurate clinical decisions. As a result, effective and efficient services pertaining to diseases diagnosis and prognosis, treatments, rehabilitation, and other patient-care tasks can be realised for improving healthcare delivery. In this edition on “Artificial Intelligence and Machine Learning for Healthcare”, a total of 21 chapters from researchers and practitioners around theworld are presented. Divided into two volumes, the first covers selected AL/ML-based image and data analytic solutions to address a variety of medical and healthcare problems, while the second presents several current methodologies and future trends in advancement of AL/ML for healthcare. There are 11 chapters in this first volume. A general overview of AI/ML is given in the first chapter. Other selected chapters describe various AL/ML models, e.g. support vector machine, convolutional neural networks, decision trees, graph-based models, for healthcare research, development, and applications from the image and data analytic perspectives. A summary of each chapter in this volume is as follows. Belciug provided an overview of AI in healthcare. The importance of AI in medicine is first elucidated. A number of commonly used AI and ML models are described, which include the decision tree, random forest, Bayesian classifier, multilayer perceptron, and convolutional neural networks. The advantages and limitations of AI for healthcare are explained, and several successful AI-based applications are described. Useful resources pertaining to AI and ML for healthcare and related domains are also presented.