Giordano Laura, Pagana Antonio Gaetano, Minciullo Paola Lucia, Fazio Manlio, Stagno Fabio, Gangemi Sebastiano, Genovese Sara, Allegra Alessandro
Division of Hematology, Department of Human Pathology in Adulthood and Childhood "Gaetano Barresi", University of Messina, Via Consolare Valeria, 98125 Messina, Italy.
Allergy and Clinical Immunology Unit, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria, 98125 Messina, Italy.
Int J Mol Sci. 2025 Jun 25;26(13):6100. doi: 10.3390/ijms26136100.
Hemophilia, an X-linked bleeding disorder, is characterized by a deficiency in coagulation factors. It manifests as spontaneous bleeding, leading to severe complications if not properly managed. In contrast, acquired hemophilia is an autoimmune condition marked by the development of inhibitory antibodies against coagulation factors. Both forms present significant diagnostic and therapeutic challenges, highlighting the need for advanced genetic, molecular, laboratory, and clinical assessments. Recent advances in artificial intelligence have opened new avenues for the management of hemophilia. Machine learning and deep learning technologies enhance the ability to predict bleeding risks, optimize treatment regimens, and monitor disease progression with greater precision. Artificial intelligence-driven applications in medical imaging have also improved the detection of joint damage and hemarthrosis, ensuring timely interventions and better clinical outcomes. Moreover, the integration of artificial intelligence into clinical practice holds the potential to transform hemophilia care through predictive analytics and personalized medicine, promising not only faster and more accurate diagnoses but also a reduction in long-term complications. However, ethical considerations and the need for data standardization remain critical for its widespread adoption. The application of artificial intelligence in hemophilia represents a paradigm shift towards precision medicine, with the promise of significantly improving patient outcomes and quality of life.
血友病是一种X连锁隐性出血性疾病，其特征是凝血因子缺乏。它表现为自发性出血，如果管理不当会导致严重并发症。相比之下，获得性血友病是一种自身免疫性疾病，其特征是产生针对凝血因子的抑制性抗体。这两种形式都带来了重大的诊断和治疗挑战，凸显了先进的基因、分子、实验室和临床评估的必要性。人工智能的最新进展为血友病的管理开辟了新途径。机器学习和深度学习技术提高了预测出血风险、优化治疗方案以及更精确地监测疾病进展的能力。人工智能驱动的医学成像应用也改善了关节损伤和关节积血的检测，确保了及时干预并带来更好的临床结果。此外，将人工智能整合到临床实践中有可能通过预测分析和个性化医疗改变血友病护理，不仅有望实现更快、更准确的诊断，还能减少长期并发症。然而，伦理考量和数据标准化需求对于其广泛应用仍然至关重要。人工智能在血友病中的应用代表了向精准医学的范式转变，有望显著改善患者预后和生活质量。
