RHEUMATIC DISEASE CLINICS OF NORTH AMERICA北美风湿病学临床
RHEUMATIC DISEASE CLINICS OF NORTH AMERICA(英文缩写 RHEUM DIS CLIN N AM),ISSN 0889-857X,eISSN 1558-3163,中文译名:北美风湿病学临床 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 2.032 | Q4 |
| 2022 | 2.300 | Q4 |
| 2023 | 2.600 | Q2 |
| 2024 | 2.900 | Q2 |
| 2025 | 1.700 | Q3 |
RHEUMATIC DISEASE CLINICS OF NORTH AMERICA 最新收录文献
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1. Essential Informatics Tools and Computing Infrastructure for Big Data to Advance Artificial Intelligence in Rheumatology.
1. 推动风湿病学人工智能发展的大数据核心信息学工具与计算基础设施PMID:日期:2026-07-06Rheumatic diseases are chronic, heterogeneous, and longitudinal, and assembling real-world evidence for effectiveness and safety for their study is best served by integrating diverse data types. This article describes the infrastructure required to support scalable, trustworthy artificial intelligence (AI) in rheumatology, emphasizing data acquisition, harmonization, linkage, privacy protection, and computational environments. We outline computing infrastructure considerations relevant to rheumatology, including hybrid on-premises and cloud architectures. Sustained progress for AI applied to rheumatology will depend on deliberate investment in shared infrastructure, longitudinal data ecosystems, and governance models that balance innovation, privacy, reproducibility, and equitable clinical value.
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2. The Role of Artificial Intelligence in Medical Education and Training: Implications for Rheumatology.
PMID:日期:2026-06-10Artificial intelligence (AI) is revolutionizing our approach to medical care in Rheumatology. From significant forthcoming changes in clinical care approaches, to changes in patient perspectives and the way they approach care, to our training approaches, changes in educational needs will be extensive. Similarly, AI will greatly support many components of training physicians but also comes with many potential concerns. Ethical expertise around utilization of AI, along with guidelines to guide practice are needed. Moreover, extensive research will be needed to help achieve these goals and support the successful integration of AI into rheumatology practice.
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3. Transforming Rheumatology Practice: Applications of Generative Artificial Intelligence.
3. 转变风湿病学实践：生成式人工智能的应用PMID:日期:2026-06-08Generative artificial intelligence (GenAI) is rapidly entering clinical workflows, yet its role remains incompletely defined. This review evaluates current and emerging applications of GenAI across common rheumatology activities using a task-based framework. The authors summarize evidence on mature tools such as AI scribes, emerging applications such as chart summarization and information extraction tools, and future opportunities in clinical prediction. While GenAI consistently reduces cognitive burden and improves documentation-related experience, efficiency gains are modest and important risks, especially automation bias and hallucinations, remain. With careful oversight and evaluation, GenAI has significant potential to enhance rheumatology practice.
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4. Demystifying Artificial Intelligence: Key Concepts with Examples in Rheumatology.
PMID:日期:2026-06-01Artificial intelligence (AI) refers to a broad class of computational methods to perform tasks that typically require human intelligence, such as learning patterns, reasoning, and problem solving. AI is increasingly applied across rheumatology research and clinical practice to analyze complex data and support clinical decision-making, yet its underlying concepts are often perceived as opaque or inaccessible. This aim of this article is to demystify foundational AI concepts by defining core terminology and describing major learning paradigms and analytical methods, highlighted through clinically relevant examples from rheumatology. We explain how modern AI models differ from traditional analytical approaches.
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7. Sources of Bias in Clinical Artificial Intelligence and Applications in Rheumatology.
PMID:日期:2026-05-25Rheumatology machine-learning models are limited by preexisting, technical, and emergent biases; the interaction of data constraints, design choices, and real-world clinical workflows, rather than from isolated technical errors. Across the model lifecycle, optimization objectives can encode patterns of care, access, and documentation, producing hidden subgroup failures that are obscured by aggregate performance metrics. Given the heterogeneity of rheumatic disease and current disparities in care delivery, addressing bias requires deliberate design choices before, during, and after a model is built, as well as a commitment to transparency, and sustained oversight.
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8. Toward Bridging the Gap from Artificial Intelligence in Clinical Research to Clinical Practice in Rheumatology: The Mayo Experience.
PMID:日期:2026-05-25This article highlights Mayo Clinic's pioneering efforts to integrate artificial intelligence (AI) and machine learning into rheumatology, focusing on genomics, imaging, pathology, and clinical data science to improve diagnosis, treatment and operational efficiency. Key innovations include transformer-based models for genomic analysis, autonomous ultrasound devices, multimodal imaging solutions, and generative AI tools for clinical documentation and patient education, all aimed at bridging the gap between research and routine clinical care. The article emphasizes the need for rigorous validation, explainable AI, electronic health records integration, clinician training, and global collaboration to ensure safe and effective adoption of AI-powered tools in clinical practice.
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9. Artificial Intelligence in Musculoskeletal Imaging: Innovations and Clinical Impact in Rheumatology.
PMID:日期:2026-05-22This article summarizes key advancements of artificial intelligence (AI) for rheumatic and musculoskeletal disease imaging in the diagnosis and classification, and predictive modeling of rheumatoid arthritis, psoriatic arthritis, spondyloarthritis, and osteoarthritis since 2020. AI applications are emerging in disease diagnosis, severity classification, and prediction of incidence and progression, with ongoing challenges related to external validation, mitigation of bias, data privacy, transparency, and clinical integration. In the near term, AI could assist clinicians with diagnostic interpretation and disease monitoring. Future applications include improved prognostic modeling and identifying candidates for targeted interventions and clinical trials.
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10. Toward Artificial Intelligence-driven Clinical Decision Support Tools in Rheumatology.
PMID:日期:2026-05-22Clinical decision support systems (CDSS) have the potential to enhance rheumatology practice by assisting with differential diagnosis, treatment decisions, and predicting patient outcomes. Rheumatic conditions are complex diseases largely diagnosed clinically rather than with a predefined set of clinical, laboratory, or imaging findings. Newer artificial intelligence (AI)-driven CDSS, known as machine-learning-based systems, may be better suited for applications to rheumatic conditions compared to knowledge-based systems, which rely on a set of predefined rules. This report reviews existing studies on CDSS in rheumatology and highlights the potential benefits and pitfalls of incorporating AI-driven CDSS into rheumatology practice.