Anoop V. Advances in Artificial Intelligence for Healthcare Applications 2025

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Anoop V. Advances in Artificial Intelligence for Healthcare Applications 2025

Textbook in PDF format Advances in Artificial Intelligence for Healthcare Applications comprehensively covers the theoretical foundations, applications, and research potential of Artificial Intelligence in the healthcare domain. Artificial Intelligence (AI) is a simulation of computerised algorithms to disjoin complicated data. The application of AI in the medical profession rises in tandem with the complexity of data in the healthcare industry. The most favourable use of AI in clinical diagnosis is diagnostic imaging. Several classifiers have been employed in this study to classify images captured at the microscopic level. The suggested architecture takes advantage of four cutting-edge Convolutional Neural Networks (CNNs) (i.e., GoogLeNet, Darknet53, Inception-ResNet-v2, and Xception) to combine the features they acquired in respective feature extraction layers. Binary tree, SVM, K-NN, NAIVE BAYES, and Neural Network are the five Machine Learning techniques that are used to provide classification outputs from the combined features. Features Discusses advanced concepts such as biomedical large language models, and natural language processing applications Covers machine vision applications for robotics in healthcare, challenges, and trends in rehabilitation devices in healthcare, and robotic interactions and control for wearable devices Presents the Internet of Things-based disease monitoring systems, Internet of nano-things for healthcare applications, and wearable Medical Internet of Things devices for accessible healthcare services Explains the use of Artificial Intelligence in bone and brain imaging, molecular imaging using artificial intelligence, and medical image segmentation Illustrates the importance of using generative artificial intelligence for clinical documentation, and medical imaging applications using generative artificial intelligence The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, Computer Science and engineering, and biomedical engineering