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Applications of Artificial Intelligence in Medicine Practice

Edited by
June 2022
184 pages
  • ISBN978-3-0365-4423-6 (Hardback)
  • ISBN978-3-0365-4424-3 (PDF)
https://doi.org/10.3390/books978-3-0365-4424-3
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This book is a reprint of the Special Issue Applications of Artificial Intelligence in Medicine Practice that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
computational intelligence; medical assistance; instance-based learning; healthcare; clinical decision support systems; deep neural networks; medical imaging; backdoor attacks; security and privacy; COVID-19; gastric cancer; endoscopy; deep learning; convolutional neural network; brain; pituitary adenoma; dysembryoplastic neuroepithelial tumor; DNET; ganglioglioma; deep learning; digital pathology; convolutional neural network; computer vision; machine learning; convolutional neural network; CNN; ATLAS; HarDNet; Swin transformer; segmentation; U-Net; cerebral infarction; CycleGAN; deep learning; machine learning; advanced statistics; schizophrenia; aggression; forensic psychiatry; medical image segmentation; CT image segmentation; deep learning; kernel density; semi-automated labeling tool; Bayesian learning; neuroimaging; feature selection; kernel formulation; mental disorders; schizophrenia; MRI; visual acuity; fundus images; machine learning; ophthalmology; deep learning; SVM; n/a