Artificial Intelligence in Hemophilia Management: Revolutionizing Patient Care and Future Directions.

Journal: Acta haematologica
Published Date: Jun 24, 2025

Abstract

Recent advancements in artificial intelligence (AI) hold significant promise for transforming hemophilia care. This review explores AI's impact on critical aspects of hemophilia management, including bleeding risk prediction, biomarker identification, personalized treatment strategies, and patient education. We discuss the application of machine learning models in predicting bleeding risks among children with hemophilia engaging in physical activities, the use of AI in analyzing factor VIII protein structures to determine disease severity, and the development of AI-powered chatbots and digital platforms for patient education and self-management, particularly in resource-limited settings. Furthermore, we address the challenges inherent in implementing AI technologies in clinical practice, such as data privacy concerns, model interpretability, and the need for robust validation. By highlighting current advancements and future directions, we underscore AI's potential to enhance personalized care and improve outcomes for individuals with hemophilia.

Authors

  • Sarina Levy-Mendelovich
  • Benjamin S Glicksberg
    The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, 770 Lexington Ave, 15th Fl, New York, NY, 10065, USA.
  • Shelly Soffer
    From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.).
  • Moran Gendler
  • Orly Efros
    Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; National Hemophilia Center and Institute of Thrombosis & Hemostasis, Chaim Sheba Medical Center, Tel Hashomer, Israel.
  • Eyal Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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