Natural language processing (NLP), a subclass of artificial intelligence, large language models (LLMs), and its latest applications, such as Generative Pre-trained Transformers (GPT), ChatGPT, or LLAMA, have recently become one of the most discussed topics. Up to now, artificial intelligence and NLP ultimately impacted several areas, such as finance, economics and diagnostic/scoring systems in healthcare. Another area that artificial intelligence has affected and will continue to affect increasingly is academic life. This narrative review will define NLP, LLMs and their applications, discuss the opportunities and challenges that components of academic society will experience in rheumatology, and discuss the impact of NLP and LLMs in rheumatology healthcare.

Previous studies have noted a possible association between periodontal diseases and the risk of various cancers. We assessed cancer risk in a cohort of patients with moderate to severe periodontitis. Patients diagnosed with moderate to severe periodontitis by a periodontist between 2001 and 2010 were identified from the hospital registry. Patients younger than 35 years of age or with a prior cancer diagnosis were excluded. The age- and gender-standardized incidence rates (SIR) were calculated by dividing the number of observed cases by the number of expected cases from Turkish National Cancer Registry 2013 data. A total of 280 patients were included (median age 49.6, 54% female). Median follow-up was 12 years. Twenty-five new cancer cases were observed. Patients with periodontitis had 77% increased risk of cancer (SIR 1.77, 95% CI 1.17-2.58, p = .004). Women with periodontitis had significantly higher risk of breast cancer (SIR 2.40, 95% CI 0.88-5.33) and men with periodontitis had significantly higher risk of prostate cancer (SIR 3.75, 95% CI 0.95-10.21) and hematological cancers (SIR 6.97, 95% CI 1.77-18.98). Although showing a causal association necessitates further investigation, our results support the idea that periodontitis might be associated with increased cancer risk, particularly with hematological, breast and prostate cancers.

To assess the real-life efficacy, retention rate, and safety data of tofacitinib in rheumatoid arthritis (RA) patients. We analyzed all patients registered in the HURBİO database who received at least 1 dose of tofacitinib. Patients who received at least one dose were included in retention analysis; patients with at least 1 control visit were included in efficacy and safety analysis. Factors predicting good response at the last follow-up visit were analyzed by logistic regression analysis. Drug retention rates were calculated using the Kaplan–Meier method and predictors of drug retention were determined by Cox proportional hazard model. Adverse events, reasons for switching, and discontinuation were also determined. Two hundred and forty-seven (210, 85.0% female) patients were included in the study. The median duration of tofacitinib treatment was 10.2 (20.2) [med, (IQR)] months. Two hundred and four (82.6%) patients were included in safety and efficacy analysis; 45.6% of patients were in low-disease activity (LDA) state (DAS28-CRP ≤ 3.2). Predictors of LDA were being biologic-naïve [aOR 2.53 (1.31–4.88); 95% CI] and RF negativity [aOR 2.14 (1.12–4.07); 95% CI]. At 1 year, the overall tofacitinib retention rate was 63.9% with no relevant predicting factor. Response and retention rates of tofacitinib were similar in patients with and without concomitant csDMARDs. Treatment failure was the most common cause of discontinuation. The most common infectious and laboratory adverse events were herpes zoster infection (3.9 per 100 patient-years) and elevation in ALT (x3UNL: 9.7 per 100 patient-years), respectively. Tofacitinib is effective as monotherapy or in combination with csDMARDs. It is a well-tolerated treatment option in Turkish RA patients.

This narrative review outlines the current applications and considerations of artificial intelligence (AI) for diagnosis, management, and prognosis in rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), and psoriatic arthritis (PsA). Advances in AI, mainly in machine learning and deep learning, have significantly influenced medical research and clinical practice over the past decades by offering precisions in data understanding and treatment approaches. AI applications have enhanced risk prediction models, early diagnosis, and better management in RA. Predictive models have guided treatment decisions such as—response to methotrexate and biologics—while wearable devices and electronic health records (EHR) improve disease activity monitoring. In addition, AI applications are reported as promising for the early identification of extra-articular involvements, prediction, detection, and assessment of comorbidities. In axSpA, AI-driven models using imaging techniques such as sacroiliac radiography, magnetic resonance imaging, and computed tomography have increased diagnostic accuracy, especially for early inflammatory changes. Predictive algorithms help stratify and predict disease outcomes, while clinical decision support systems integrate clinical and imaging data for optimized management. For PsA, AI has also allowed for early detection among psoriasis patients using genetic markers, immune profiling, and EHR-based natural language processing systems. Overall, AI models may predict diagnosis, disease severity, treatment response, and comorbidities to improve care in patients with RA, axSpA, and PsA. As a rapidly developing and improving area, AI has the potential to change our current perspective of medical practice by offering better diagnostic evaluation and treatments and improved patient follow-up. Multimodal AI, focusing on collaboration, reliability, transparency, and patient-centered innovation, looks like the future of medical practice. However, data quality, model interpretability, and ethical considerations must be addressed to ensure reliable and equitable applications in clinical practice.