Artificial intelligence (AI) is rapidly evolving and reshaping everyday life for everyone, including healthcare. The general-purpose models, such as the GPT series, Claude, Gemini, Grok, and others, have demonstrated remarkable capabilities in the solution of medical problems and presenting opportunities and challenges for medical research and clinical practice [1]. Rheumatology, characterized by complex chronic diseases, benefits a lot from rapid advances in LLMs, but must also carefully manage the associated risks.
Include applications in Table 1.
Despite rapid and extensive resource investment in AI research, significant limitations persist. First, a primary concern for general use remains the validation of AI accuracy. AI hallucination has yet to be fully resolved within current training paradigms, although numerous approaches to solve this issue have already been proposed. Second, the various training datasets lead to data bias, potentially exacerbating health inequities. Third, cloud-based deployment raises significant concerns regarding patient privacy and data security. Conversely, running AI locally is often constrained by limited computing power on personal devices, thereby further restricting its practical application. Fourth, even with current reasoning models, the internal cognitive mechanisms remain inadequately understood. Therefore, these concerns must be carefully considered when applying AI to clinical scenarios.
LLMs offer significant promise as tools for rheumatology, with potential improvement of clinical practice, medical research, and personal life, but several challenges remain. To effectively utilize these technologies, their differences in ability, accuracy, adherence to the highest ethical standards, and flexibility to continuous learning need to be carefully evaluated.
Po-Cheng Shih wrote the manuscript. James Cheng Chung Wei supervise and edit the manuscript. All authors are responsible for the final version of the manuscript.
The views expressed in this article represent those of the authors and do not necessarily reflect the official position of the institutions with which they are affiliated.
Dr. James Wei is the Editor-in-Chief of IJRD and a co-author of this article. They were excluded from editorial decision-making related to the acceptance and publication of this article. Editorial decision-making was handled independently by other editors to minimize bias. The other authors declare no conflicts of interest.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Systemic sclerosis (SSc) is a rare autoimmune disease with high mortality, often due to cardiopulmonary complications. Hydroxychloroquine (HCQ), commonly used in other rheumatic diseases, has immunomodulatory and potentially cardioprotective effects, but its role in SSc remains unclear. This study aimed to evaluate the association between HCQ use and the risk of mortality, ischemic heart disease (IHD), and pulmonary hypertension (PH) in a large real-world SSc cohort.
This retrospective cohort study utilized de-identified electronic medical records from the TriNetX Research network. Adults with an SSc diagnosis (ICD-10-CM: M34) were divided into HCQ users and non-users. After 1:1 propensity score matching for demographics, comorbidities and medications, outcomes including mortality, PH, acute myocardial infarction (MI), cerebral infarction, conduction heart disease, and myocarditis were assessed over 5 years. Risk ratios (RR) and Kaplan–Meier hazard ratios were calculated.
Out of 17 395 HCQ users and 58 576 non-users, 15 485 propensity score matched pairs were analyzed. Over 5 years, HCQ users showed higher PH (RR 1.124, 95% CI, 1.052–1.200, p < 0.001) but lower mortality (RR 0.719, 95% CI, 0.674–0.767, p < 0.001), indicating potential survival benefits. No significant differences were observed for IHD, MI, cerebral infarction, conduction disorders, or myocarditis.
Our research indicated that although patients on HCQ had a higher prevalence of PH, they exhibited lower mortality rates, suggesting a possible survival benefit. Further prospective studies are needed to explore these findings and clarify HCQ's role in SSC management.
Neutrophil extracellular traps (NETs) significantly contribute to rheumatoid arthritis (RA) pathogenesis, though their exact mechanisms remain unclear. In this research, we explored how NETs influence RA development through the PANoptosis core molecule AIM2.
Enzyme-linked immunosorbent assay and Quant-iT Pico Green measured AIM2 and cell-free DNA (cfDNA) levels in synovial fluid. Immunohistochemistry examined the levels of AIM2 in synovial tissues. In vitro, the CCK-8 assay evaluated cell proliferation. Western blot detected the expression changes of AIM2, PANoptosis-related proteins, and NF-κB pathway-related proteins. Real-time quantitative PCR measured mRNA levels of IL-1β, IL-18, and IL-6. Immunofluorescence quantified AIM2, Caspase-8, GSDMD, and p-MLKL fluorescence intensity and cfDNA/AIM2 co-localization. An AIM2-silenced cell model was established to examine changes in the AIM2, PANoptosis-related proteins, and NF-κB pathway-related proteins (Western blot), fluorescence intensity (IF), inflammatory cytokine transcription (RT-qPCR), and RA-FLS migration (scratch and transwell assays).
Our results showed an increase in AIM2 expression in RA synovial fluid and tissues, which correlates with both cfDNA high levels and clinical disease activity scores. In vitro, in RA fibroblast-like synoviocytes (RA-FLSs), NETs elevated AIM2 and PANoptosis-related protein levels, activated the NF-κB signaling pathway, and enhanced the release of inflammatory cytokines. Treatment with DNase1 and AIM2 silencing resulted in lower levels of PANoptosis proteins and AIM2, inhibited NF-κB signaling, and decreased cytokine production.
This is the first time exploring the pathogenic mechanism of NETs-induced PANoptosis in RA, and targeting AIM2 may represent a potential new therapeutic approach for RA.
Objective: This study aims to evaluate serum levels of Galectin-3, Tryptophan (TRP), and kynurenine/alpha-aminoadipate aminotransferase (AADAT) in patients with rheumatoid arthritis (RA), and to investigate their potential diagnostic and prognostic implications.
Methods: This was an original experimental study. The study population consisted of individuals followed up at the Rheumatology Polyclinic of Selçuk University Faculty of Medicine, Konya, Türkiye. Between April 2025 and July 2025, 45 RA patients diagnosed according to the ACR/EULAR 2010 criteria and 45 healthy control subjects were included in the study. Statistical analyses included t-test, Mann-Whitney U, correlation analyses, and ROC curve evaluation. Serum levels of Galectin-3, TRP, and AADAT were measured by ELISA. Disease activity was assessed using DAS-28 scores.
Results: TRP levels were significantly lower in RA patients compared to controls (p < 0.001), whereas Galectin-3 levels were significantly higher (p < 0.001). AADAT levels did not differ significantly between groups (p = 0.61). Galectin-3 and TRP showed strong diagnostic performance (AUC = 0.84 and 0.76, respectively). Significant correlations were observed among Galectin-3, TRP, and AADAT, as well as with hematological indices.
Conclusions: Elevated Galectin-3 and reduced TRP levels are associated with RA pathogenesis, whereas AADAT levels remain comparable between patients and controls. These findings suggest Galectin-3 and TRP as potential biomarkers for early diagnosis of RA.
Rheumatoid arthritis (RA) is a chronic autoimmune disorder with unclear molecular mechanisms, complicating early diagnosis and treatment. This study aimed to identify hub genes and pathways driving RA pathogenesis and assess their therapeutic potential.
Gene expression datasets related to RA were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and analyzed by functional enrichment and protein–protein interaction network construction. Machine learning approaches, including LASSO regression, random forest, and SVM-RFE, were used to screen hub genes. Pathway associations were explored using Gene Set Enrichment Analysis (GSEA). Experimental validation was performed in collagen-induced arthritis (CIA) rat models and MH7A synovial fibroblast cells through Western blot and functional assays.
A total of 106 DEGs were identified in RA synovial tissues, including 76 upregulated and 30 downregulated genes. Enrichment analyses revealed involvement in cytokine–cytokine receptor interaction, lymphocyte-mediated immunity, and immunoglobulin complexes. SDC1 emerged as a key hub gene across all three machine learning methods. GSEA indicated its significant correlation with the JAK–STAT pathway. In CIA rats, SDC1 expression was markedly elevated alongside p-JAK2 and p-STAT3 levels. Silencing SDC1 in MH7A cells reduced cell proliferation, decreased p-JAK2 and p-STAT3 expression, and promoted apoptosis.
This study identifies SDC1 as a central hub gene in RA pathogenesis through activation of the JAK2–STAT3 signaling pathway. These findings highlight SDC1 as a potential biomarker for early diagnosis and a promising target for therapeutic intervention, providing new insights into RA management.
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