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This review examines the increasing use of artificial intelligence (AI) in rheumatology, focusing on its potential impact in key areas. AI, including machine learning (ML) and deep learning (DL), is revolutionizing diagnosis, treatment personalization, and prognosis prediction in rheumatologic diseases. Specifically, AI models based on convolutional neural networks (CNNs) demonstrate significant efficacy in analyzing medical images for disease classification and severity assessment. Predictive AI models also have the ability to forecast disease trajectories and treatment responses, enabling more informed clinical decisions. The role of wearable devices and mobile applications in continuous disease monitoring is discussed, although their effectiveness varies across studies. Despite existing challenges, such as data privacy concerns and issues of model generalizability, the compelling results highlight the transformative potential of AI in rheumatologic disease management. As AI technologies continue to evolve, further research will be essential to address these challenges and fully harness the potential of AI to improve patient outcomes in rheumatology.

Background: We aimed to evaluate the baseline performance and improvement of ChatGPT-4 "omni" (ChatGPT-4o) and Gemini 1.5 Flash (Gemini 1.5) in answering multiple-choice questions related to pediatric nephrology after specific training., Methods: Using questions from the "Educational Review" articles published by Pediatric Nephrology between January 2014 and April 2024, the models were tested both before and after specific training with Portable Data Format (PDF) and text (TXT) file formats of the Educational Review articles removing the last page containing the correct answers using a Python script. The number of correct answers was recorded., Results: Before training, ChatGPT-4o correctly answered 75.2% of the 1395 questions, outperforming Gemini 1.5, which answered 64.9% correctly (p < 0.001). After training with PDF files, ChatGPT-4o's accuracy increased to 77.8%, while Gemini 1.5 improved significantly to 84.7% (p < 0.001). Training with TXT files showed similar results, with ChatGPT-4o maintaining 77.8% accuracy and Gemini 1.5 further improving to 87.6% (p < 0.001)., Conclusions: The study highlights that while ChatGPT-4o has strong baseline performance, specific training does not significantly enhance its accuracy. Conversely, Gemini 1.5, despite its lower initial performance, shows substantial improvement with training, particularly with TXT files. These findings suggest Gemini 1.5's superior ability to store and retrieve information, making it potentially more effective in clinical applications, albeit with a dependency on additional data for optimal performance., (© 2024. The Author(s).)

Asthma, a prevalent chronic respiratory condition characterized by inflammation of the airways and bronchoconstriction, has demonstrated a potential association with hemoglobinopathies such as thalassemia and sickle cell disease (SCD). Numerous studies have highlighted a higher prevalence of asthma among thalassemia patients compared to the general population, with rates ranging around 30%. Similarly, asthma frequently coexists with SCD, affecting approximately 20-48% of patients. Children with SCD often experience heightened lower airway obstruction and airway hyper-reactivity. Notably, the presence of asthma in SCD exacerbates respiratory symptoms and increases the risk of severe complications like acute chest syndrome, stroke, vaso-occlusive episodes, and early mortality. Several studies have noted a decrease in various cytokines such as IFN-γ and IL-10, along with higher levels of both IL-6 and IL-8, suggesting an overactivation of pro-inflammatory mechanisms in patients with hemoglobinopathies, which could trigger inflammatory conditions such as asthma. The exact mechanisms driving this association are better elucidated but may involve factors such as chronic inflammation, oxidative stress, and immune dysregulation associated with thalassemia-related complications like chronic hemolytic anemia and iron overload. This review aims to comprehensively analyze the relationship between asthma and hemoglobinopathies, with a focus on thalassemia and SCD. It emphasizes the importance of interdisciplinary collaboration among pulmonologists, hematologists, and other healthcare professionals to effectively manage this complex interplay. Understanding this link is crucial for improving care and outcomes in affected individuals., Competing Interests: The authors declare no conflicts of interest.