RESUMEN
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple organs and systems. It is characterized by the production of abnormal antibodies that attack healthy cells and tissues. The disease presents a wide range of symptoms and severity, from mild to severe. Diagnosis can be complex, but the classification criteria of the American College of Rheumatology (ACR) help to facilitate it. Incidence and prevalence vary considerably worldwide, mainly affecting adult women between the third and fourth decades of life, although it can also occur in childhood. The prognosis of SLE has improved over time, but there is still a risk of irreversible organ damage. Treatment is individualized for each patient and is based on immunosuppression and the use of corticosteroids. Biological therapies, such as monoclonal antibodies, have emerged as a more specific alternative. Methotrexate, antimalarials, glucocorticoids, immunosuppressants, and monoclonal antibodies are some of the medications used to treat SLE. New therapeutic strategies are currently being developed, such as targeted therapies, immunomodulators, and biological agents. Treatment adherence, monitoring, and regular follow-up are important aspects of SLE management. This article aims to describe the characteristics of the new monoclonal antibody therapies that exist for the management of SLE.
RESUMEN
Qure.AI, a leading company in artificial intelligence (AI) applied to healthcare, has developed a suite of innovative solutions to revolutionize medical diagnosis and treatment. With a plethora of FDA-approved tools for clinical use, Qure.AI continually strives for innovation in integrating AI into healthcare systems. This article delves into the efficacy of Qure.AI's chest X-ray interpretation tool, "qXR," in medicine, drawing from a comprehensive review of clinical trials conducted by various institutions. Key applications of AI in healthcare include machine learning, deep learning, and natural language processing (NLP), all of which contribute to enhanced diagnostic accuracy, efficiency, and speed. Through the analysis of vast datasets, AI algorithms assist physicians in interpreting medical data and making informed decisions, thereby improving patient care outcomes. Illustrative examples highlight AI's impact on medical imaging, particularly in the diagnosis of conditions such as breast cancer, heart failure, and pulmonary nodules. AI can significantly reduce diagnostic errors and expedite the interpretation of medical images, leading to more timely interventions and treatments. Furthermore, AI-powered predictive analytics enable early detection of diseases and facilitate personalized treatment plans, thereby reducing healthcare costs and improving patient outcomes. The efficacy of AI in healthcare is underscored by its ability to complement traditional diagnostic methods, providing physicians with valuable insights and support in clinical decision-making. As AI continues to evolve, its role in patient care and medical research is poised to expand, promising further advancements in diagnostic accuracy and treatment efficacy.