The use of artificial intelligence in the prevention and management of bleeding disorders: a systematic review.

Background: Bleeding disorders, including hemophilia, von Willebrand disease (VWD), and immune thrombocytopenia (ITP), pose significant diagnostic and therapeutic challenges due to their heterogeneous presentations and complex underlying mechanisms. Traditional diagnostic methods rely on clinical assessments and laboratory tests, which can be time-consuming and prone to misdiagnosis, particularly in resource-limited settings. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, leveraging machine learning (ML) algorithms and predictive analytics to enhance diagnostic accuracy, risk stratification, and personalized treatment approaches. Objective: This systematic review explores the role of AI in the prevention, diagnosis, and management of bleeding disorders. Specifically, it assesses AI-driven models in identifying key predictors, optimizing risk assessment, and improving treatment outcomes. Methods: A comprehensive literature search was conducted across major databases following PRISMA guidelines. Studies were selected based on their focus on AI applications in bleeding disorders, particularly those utilizing ML models such as Random Forest, XGBoost, LightGBM, and deep learning techniques. The risk of bias was evaluated using the ROBINS-E and RoB 2 tools. Results: Twelve studies met the inclusion criteria, demonstrating the efficacy of AI models in bleeding disorder management. Genetic markers, such as Factor VIII gene mutations and von Willebrand factor variants, enable early disease classification and severity prediction. Laboratory biomarkers, including baseline factor VIII activity, platelet count, and coagulation profiles, enhance risk assessment for bleeding complications. Clinical history variables, such as prior bleeding events, anticoagulant use, infection status, and comorbidities, support personalized treatment strategies. Additionally, demographic and environmental factors, including age, sex, healthcare utilization patterns, and socioeconomic status, refine predictive models for undiagnosed cases. Conclusion: The integration of these variables into AI-driven models has demonstrated superior diagnostic accuracy compared to traditional methods, facilitating early detection, individualized treatment planning, and improved patient outcomes. However, challenges such as dataset fragmentation, model interpretability, and limited external validation hinder widespread clinical adoption. AI-driven approaches have the potential to revolutionize bleeding disorder management by advancing precision medicine, optimizing healthcare resources, and promoting equitable access to high-quality care.