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.

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.

Artificial Intelligence (AI) and Precision Medicine are increasingly influencing pediatric pharmacotherapy, where age-dependent pharmacokinetic variability demands highly individualized therapeutic strategies. This review examines current applications of AI in pediatric precision medicine and evaluates their clinical relevance and translational challenges. Recent evidence shows substantial progress across multiple domains. In pharmacogenomics, predictive models have reached R² = 0.95 for drug exposure. Tools for adverse drug reaction detection report sensitivities of 81.5% and specificities of 79.5%. Clinical decision support systems for pediatric epilepsy have achieved diagnostic accuracies of 93.4%. Real-world implementations have been associated with a 75% reduction in prescription distribution errors and a 65% improvement in adverse drug reaction detection. Despite these advances, clinical translation remains limited: only 0.38% of pediatric AI models progress to testing in real patients, and 77% of published studies carry a high risk of bias. These gaps highlight the need for rigorous validation, improved data quality, and careful consideration of ethical and algorithmic constraints. Overall, AI has the potential to shift pediatric pharmacotherapy from empirically driven decisions toward predictive, precision-based approaches. Achieving this goal will require well-designed pediatric studies and sustained interdisciplinary collaboration to ensure safe and effective integration into clinical practice.

Artificial Intelligence (AI) and Precision Medicine represent foundational pillars for transforming pediatric healthcare, as children exhibit age-specific pharmacokinetic variations requiring highly personalized therapeutic approaches that make AI an indispensable tool for optimizing pharmacological safety and efficacy. This review analyzes current AI applications in pediatric precision pharmacotherapy, examining clinical opportunities and implementation challenges. AI demonstrates tangible clinical impact across multiple domains: pharmacogenomics with predictive models achieving R² = 0.95 for drug exposure; adverse drug reaction prediction with 81.5% sensitivity and 79.5% specificity; clinical decision support systems with 93.4% accuracy in pediatric epilepsy diagnosis. AI implementation has reduced prescription distribution errors by 75% and improved adverse drug reaction detection by 65%. However, significant gaps persist as only 0.38% of pediatric AI models reach clinical testing level, and 77% of studies show high risk of bias. AI transforms pediatric pharmacotherapy from empirical approaches to evidence-based predictive strategies, converting pediatric vulnerability into an innovation catalyst. The technology shifts understanding from correlation to causality, enabling personalized dosing and transforming pharmacovigilance into proactive safety mechanisms. Successful implementation requires overcoming current limitations including algorithmic bias, data quality issues, ethical considerations, and validation through rigorous clinical studies specifically designed for pediatric populations. Future development of sophisticated AI models promises enhanced precision, but real-world validation through interdisciplinary collaboration remains imperative for building robust pediatric AI ecosystems that opti-mize therapeutic outcomes for this vulnerable population.