Advances in AI for Health and Medical Applications
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 deli...
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| Materyal Türü: | Online |
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| Dil: | İngilizce |
| Baskı/Yayın Bilgisi: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Konular: | |
| Online Erişim: | ONIX_20240514_9783725803675_250 |
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| _version_ | 1869519406098284544 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-137652 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1376522024-05-14T13:56:51Z Advances in AI for Health and Medical Applications Liu, Sidong Olea, Cristián Castillo Berkovsky, Shlomo 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 thema EDItEUR::U Computing and Information Technology::UY Computer science 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. 2024-05-14T13:56:46Z 2024-05-14T13:56:46Z 2024 book ONIX_20240514_9783725803675_250 9783725803675 9783725803682 https://directory.doabooks.org/handle/20.500.12854/137652 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8851 https://mdpi.com/books/pdfview/book/8851 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0368-2 10.3390/books978-3-7258-0368-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725803675 9783725803682 214 open access |
| spellingShingle | 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 thema EDItEUR::U Computing and Information Technology::UY Computer science Advances in AI for Health and Medical Applications |
| title | Advances in AI for Health and Medical Applications |
| title_full | Advances in AI for Health and Medical Applications |
| title_fullStr | Advances in AI for Health and Medical Applications |
| title_full_unstemmed | Advances in AI for Health and Medical Applications |
| title_short | Advances in AI for Health and Medical Applications |
| title_sort | advances in ai for health and medical applications |
| topic | 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 thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | 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 thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240514_9783725803675_250 |