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A survey of artificial intelligence in rheumatoid arthritis
The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
De Gruyter
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362600/ https://www.ncbi.nlm.nih.gov/pubmed/37485476 http://dx.doi.org/10.2478/rir-2023-0011 |
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author | Wang, Jiaqi Tian, Yu Zhou, Tianshu Tong, Danyang Ma, Jing Li, Jingsong |
author_facet | Wang, Jiaqi Tian, Yu Zhou, Tianshu Tong, Danyang Ma, Jing Li, Jingsong |
author_sort | Wang, Jiaqi |
collection | PubMed |
description | The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption. |
format | Online Article Text |
id | pubmed-10362600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | De Gruyter |
record_format | MEDLINE/PubMed |
spelling | pubmed-103626002023-07-23 A survey of artificial intelligence in rheumatoid arthritis Wang, Jiaqi Tian, Yu Zhou, Tianshu Tong, Danyang Ma, Jing Li, Jingsong Rheumatol Immunol Res Review The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption. De Gruyter 2023-07-22 /pmc/articles/PMC10362600/ /pubmed/37485476 http://dx.doi.org/10.2478/rir-2023-0011 Text en © 2023 Jiaqi Wang, Yu Tian, Tianshu Zhou, Danyang Tong, Jing Ma, Jingsong Li, published by De Gruyter on behalf of the SMP https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. |
spellingShingle | Review Wang, Jiaqi Tian, Yu Zhou, Tianshu Tong, Danyang Ma, Jing Li, Jingsong A survey of artificial intelligence in rheumatoid arthritis |
title | A survey of artificial intelligence in rheumatoid arthritis |
title_full | A survey of artificial intelligence in rheumatoid arthritis |
title_fullStr | A survey of artificial intelligence in rheumatoid arthritis |
title_full_unstemmed | A survey of artificial intelligence in rheumatoid arthritis |
title_short | A survey of artificial intelligence in rheumatoid arthritis |
title_sort | survey of artificial intelligence in rheumatoid arthritis |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362600/ https://www.ncbi.nlm.nih.gov/pubmed/37485476 http://dx.doi.org/10.2478/rir-2023-0011 |
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