Advances in AI for Health and Medical Applications

doi:10.3390/books978-3-7258-0368-2

Download Url(s)

https://mdpi.com/books/pdfview/book/8851

Contributor(s)

Liu, Sidong (editor)

Olea, Cristián Castillo (editor)

Berkovsky, Shlomo (editor)

Language

English

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Abstract

The past decade has witnessed an explosive growth in the development and use of artificial intelligence (AI) across diverse fields; healthcare is no exception. In fact, AI is at the forefront of driving pivotal changes in the healthcare sector, opening up innovative and enhanced methods of care delivery. It holds the potential to have profound impacts on contemporary healthcare challenges. By leveraging AI, we can uncover patterns within vast clinical datasets and develop sophisticated computational reasoning methods that support human decision making. This Special Issue endeavours to spotlight the cutting-edge developments of AI in the healthcare and medical fields, and it proudly features twelve manuscripts encompassing a diverse array of original research and review articles. The collection of articles span from theoretical frameworks to practical applications, addressing everything from diagnosis and treatment to healthcare management and public health.

URI

https://directory.doabooks.org/handle/20.500.12854/137652

Keywords

colon cancer; deep learning; detection; classification; localization; CNN; autoencoders; chest CT; COVID-19; severity assessment; progression prediction; U-Net; RNN; machine learning; identification; HIV; e-Clinical assistance; outcome prediction; multi-modal medical image; image classification; brain tumor; AI-powered behavioral change support systems; motivation; computational modeling; behavior change techniques; AI in health; pervasive health system; affective; depression screening; digital phenotype; emotion; passive sensing; wavelet transforms; wearable devices; emergency department; temperature; older adult; Hong Kong; fuzzy knowledge graph; FKG-Pairs; disease diagnosis; preeclampsia; decision making; semantic segmentation; multi-class; 3D image stacks; region of interest; Dice score; Unet; CT images; overfitting; diabetes mellitus; survey; feature selection; feature importance; public health; hospital; patient; community; artificial intelligence; n/a