The emergence of large language models (LLM) with remarkable performance such as ChatGPT and GPT-4, has led to an unprecedented uptake in the population. One of their most promising and studied applications concerns education due to their ability to understand and generate human-like text, creating a multitude of opportunities for enhancing educational practices and outcomes. The objective of this study is twofold: to assess the accuracy of ChatGPT/GPT-4 in answering rheumatology questions from the access exam to specialized medical training in Spain (MIR), and to evaluate the medical reasoning followed by these LLM to answer those questions. A dataset, RheumaMIR, of 145 rheumatology-related questions, extracted from the exams held between 2010 and 2023, was created for that purpose, used as a prompt for the LLM, and was publicly distributed. Six rheumatologists with clinical and teaching experience evaluated the clinical reasoning of the chatbots using a 5-point Likert scale and their degree of agreement was analyzed. The association between variables that could influence the models’ accuracy (i.e., year of the exam question, disease addressed, type of question and genre) was studied. ChatGPT demonstrated a high level of performance in both accuracy, 66.43%, and clinical reasoning, median (Q1–Q3), 4.5 (2.33–4.67). However, GPT-4 showed better performance with an accuracy score of 93.71% and a median clinical reasoning value of 4.67 (4.5–4.83). These findings suggest that LLM may serve as valuable tools in rheumatology education, aiding in exam preparation and supplementing traditional teaching methods.

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

The last 2 years have marked the beginning of a golden age for natural language processing in medicine. The arrival of large language models (LLMs) and multimodal models have raised new opportunities and challenges for research and clinical practice. In rheumatology, a specialty rich in data and requiring complex decision-making, the use of these tools may transform diagnostic procedures, improve patient interaction and simplify data management, leading to more personalized and efficient healthcare outcomes. The objective of this article is to present an overview of the status of LLMs in the field of rheumatology while discussing some of the challenges ahead in this area of great potential.

Background: Rheumatology has experienced notable changes in the last decades. New drugs, including biologic agents and Janus kinase (JAK) inhibitors, have blossomed. Concepts such as window of opportunity, arthralgia suspicious for progression, or difficult-to-treat rheumatoid arthritis (RA) have appeared; and new management approaches and strategies such as treat-to-target have become popular. Statistical learning methods, gene therapy, telemedicine, or precision medicine are other advancements that have gained relevance in the field. To better characterize the research landscape and advances in rheumatology, automatic and efficient approaches based on natural language processing (NLP) should be used. Objectives: The objective of this study is to use topic modeling (TM) techniques to uncover key topics and trends in rheumatology research conducted in the last 23 years. Design: Retrospective study. Methods: This study analyzed 96,004 abstracts published between 2000 and December 31, 2023, drawn from 34 specialized rheumatology journals obtained from PubMed. BERTopic, a novel TM approach that considers semantic relationships among words and their context, was used to uncover topics. Up to 30 different models were trained. Based on the number of topics, outliers, and topic coherence score, two of them were finally selected, and the topics were manually labeled by two rheumatologists. Word clouds and hierarchical clustering visualizations were computed. Finally, hot and cold trends were identified using linear regression models. Results: Abstracts were classified into 45 and 47 topics. The most frequent topics were RA, systemic lupus erythematosus, and osteoarthritis. Expected topics such as COVID-19 or JAK inhibitors were identified after conducting dynamic TM. Topics such as spinal surgery or bone fractures have gained relevance in recent years; however, antiphospholipid syndrome or septic arthritis have lost momentum. Conclusion: Our study utilized advanced NLP techniques to analyze the rheumatology research landscape and identify key themes and emerging trends. The results highlight the dynamic and varied nature of rheumatology research, illustrating how interest in certain topics has shifted over time.

Timely access to current rheumatology guidelines at the point of care is challenging. We aimed to develop and evaluate the first retrieval-augmented generation (RAG) system designed for adult rheumatology, integrating European Alliance of Associations for Rheumatology (EULAR) and American College of Rheumatology (ACR) guidelines to provide rheumatologists with timely evidence-based recommendations. EULAR and ACR management guidelines were selected by rheumatologists based on their clinical relevance for decision-making and processed. A RAG system was implemented. To evaluate it, 10 questions per guideline were generated using ChatGPT 4.5. Answers to these were produced by ChatGPT-o3-mini with context retrieval (RAG) and without (baseline). Performance was assessed by an Large language model (LLM)-as-a-judge (Gemini 2.0 Flash) using a 5-point Likert scale across 5 dimensions: relevance, factual accuracy, safety, completeness, and conciseness; it also determined preference between the RAG and baseline responses. For validation, 2 rheumatologists independently evaluated a random sample of questions (15%) on the same domains. Statistical significance was established using the Wilcoxon signed-rank and binomial tests. Seventy-four guidelines were included, yielding 740 evaluation questions. The LLM-as-a-judge evaluation showed the RAG system significantly outperformed the baseline across all criteria (P < .001). Manual evaluation confirmed these findings (P < .001) for accuracy, safety, and completeness. The RAG system was significantly preferred by the LLM-as-a-judge in 92.8% of comparisons and by the human evaluators in 71.2% to 74.8% (P < .001). Developing a RAG system integrating extensive EULAR/ACR rheumatology guidelines improves answer quality compared to a baseline LLM. This evaluation provides a robust foundation for reliable, artificial intelligence-driven clinical decision support tools designed to enhance evidence-based practice by providing clinicians with rapid, context-aware access to recommendations.

Our objective is to develop and validate a predictive model based on the random forest algorithm to estimate the readmission risk to an outpatient rheumatology clinic after discharge. We included patients from the Hospital Clínico San Carlos rheumatology outpatient clinic, from 1 April 2007 to 30 November 2016, and followed-up until 30 November 2017. Only readmissions between 2 and 12 months after the discharge were analyzed. Discharge episodes were chronologically split into training, validation, and test datasets. Clinical and demographic variables (diagnoses, treatments, quality of life (QoL), and comorbidities) were used as predictors. Models were developed in the training dataset, using a grid search approach, and performance was compared using the area under the receiver operating characteristic curve (AUC-ROC). A total of 18,662 discharge episodes were analyzed, out of which 2528 (13.5%) were followed by outpatient readmissions. Overall, 38,059 models were developed. AUC-ROC, sensitivity, and specificity of the reduced final model were 0.653, 0.385, and 0.794, respectively. The most important variables were related to follow-up duration, being prescribed with disease-modifying anti-rheumatic drugs and corticosteroids, being diagnosed with chronic polyarthritis, occupation, and QoL. We have developed a predictive model for outpatient readmission in a rheumatology setting. Identification of patients with higher risk can optimize the allocation of healthcare resources.