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REVIEW article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Volume 15 - 2024 |
doi: 10.3389/fimmu.2024.1477130
Artificial Intelligence for Predicting Treatment Responses in Rheumatic Auto-immune Diseases: Advancements, Challenges, and Future Perspectives
Provisionally accepted- 1 Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, China
- 2 Xinzhou People's Hospital, Xin Zhou, China
- 3 Guangzhou Second Provincial General Hospital, Guangzhou, China
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.
Keywords: artificial intelligence, machine learning, Auto-immune rheumatism, Therapeutic response, deep learning
Received: 07 Aug 2024; Accepted: 03 Oct 2024.
Copyright: © 2024 Yang, Liu, Chen, Luo, Xu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yanli Yang, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, China
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