Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives
Autoimmune rheumatic diseases (ARD) present a significant global health challenge characterized by a rising prevalence. These highly heterogeneous diseases involve complex pathophysiological mechanisms, leading to variable treatment efficacies across individuals. This variability underscores the nee...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-10-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1477130/full |
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| author | Yanli Yang Yang Liu Yu Chen Di Luo Ke Xu Liyun Zhang |
| author_facet | Yanli Yang Yang Liu Yu Chen Di Luo Ke Xu Liyun Zhang |
| author_sort | Yanli Yang |
| collection | DOAJ |
| description | Autoimmune rheumatic diseases (ARD) present a significant global health challenge characterized by a rising prevalence. These highly heterogeneous diseases involve complex pathophysiological mechanisms, leading to variable treatment efficacies across individuals. This variability underscores the need for personalized and precise treatment strategies. Traditionally, clinical practices have depended on empirical treatment selection, which often results in delays in effective disease management and can cause irreversible damage to multiple organs. Such delays significantly affect patient quality of life and prognosis. Artificial intelligence (AI) has recently emerged as a transformative tool in rheumatology, offering new insights and methodologies. Current research explores AI’s capabilities in diagnosing diseases, stratifying risks, assessing prognoses, and predicting treatment responses in ARD. These developments in AI offer the potential for more precise and targeted treatment strategies, fostering optimism for enhanced patient outcomes. This paper critically reviews the latest AI advancements for predicting treatment responses in ARD, highlights the current state of the art, identifies ongoing challenges, and proposes directions for future research. By capitalizing on AI’s capabilities, researchers and clinicians are poised to develop more personalized and effective interventions, improving care and outcomes for patients with ARD. |
| format | Article |
| id | doaj-art-ce8b7201d5954035a472bfcb4e892f84 |
| institution | OA Journals |
| issn | 1664-3224 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-ce8b7201d5954035a472bfcb4e892f842025-08-20T02:08:42ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-10-011510.3389/fimmu.2024.14771301477130Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectivesYanli Yang0Yang Liu1Yu Chen2Di Luo3Ke Xu4Liyun Zhang5Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, ChinaThird Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, ChinaDepartment of Emergency Medicine, Xinzhou People’s Hospital, Xinzhou, ChinaDepartment of Health Management, Guangdong Second Provincial General Hospital, Guangzhou, ChinaThird Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, ChinaThird Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, ChinaAutoimmune rheumatic diseases (ARD) present a significant global health challenge characterized by a rising prevalence. These highly heterogeneous diseases involve complex pathophysiological mechanisms, leading to variable treatment efficacies across individuals. This variability underscores the need for personalized and precise treatment strategies. Traditionally, clinical practices have depended on empirical treatment selection, which often results in delays in effective disease management and can cause irreversible damage to multiple organs. Such delays significantly affect patient quality of life and prognosis. Artificial intelligence (AI) has recently emerged as a transformative tool in rheumatology, offering new insights and methodologies. Current research explores AI’s capabilities in diagnosing diseases, stratifying risks, assessing prognoses, and predicting treatment responses in ARD. These developments in AI offer the potential for more precise and targeted treatment strategies, fostering optimism for enhanced patient outcomes. This paper critically reviews the latest AI advancements for predicting treatment responses in ARD, highlights the current state of the art, identifies ongoing challenges, and proposes directions for future research. By capitalizing on AI’s capabilities, researchers and clinicians are poised to develop more personalized and effective interventions, improving care and outcomes for patients with ARD.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1477130/fullartificial intelligencemachine learningautoimmune rheumatic diseasestherapeutic responsedeep learning |
| spellingShingle | Yanli Yang Yang Liu Yu Chen Di Luo Ke Xu Liyun Zhang Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives Frontiers in Immunology artificial intelligence machine learning autoimmune rheumatic diseases therapeutic response deep learning |
| title | Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives |
| title_full | Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives |
| title_fullStr | Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives |
| title_full_unstemmed | Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives |
| title_short | Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives |
| title_sort | artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases advancements challenges and future perspectives |
| topic | artificial intelligence machine learning autoimmune rheumatic diseases therapeutic response deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1477130/full |
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