CO-10 | APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEMOPHILIA MANAGEMENT: INNOVATIONS AND FUTURE PERSPECTIVES

A. Ierardi, M. Leotta, A. Strangio, S. Prejanò, R.C. Santoro | UO Hemophilia, Hemostasis and Thrombosis. Azienda Ospedaliero Universitaria Renato Dulbecco, Catanzaro, Italy

Background. Hemophilia care requires a highly personalized paradigm. Despite EHL products, individual PK variability and bleeding phenotypes remain challenging. AI integration now enables a shift from reactive management to a predictive, precision-based clinical strategy. This study hypothesis intends to examine the transformative role and possible applications of AI within the multidisciplinary management of hemophilia, focusing on the optimization of prophylaxis, prevention of joint damage, and enhancement of therapeutic adherence. and its possible applications.
Methods and Areas of Application. Predictive Analytics and Risk Stratification. The use of supervised learning algorithms enables the analysis of complex datasets integrating clinical history, baseline factor levels, bleeding phenotypes, and physical activity. Developing predictive models allows for the early identification of patients at higher risk of breakthrough bleeding, facilitating targeted preventive interventions. Clinical Pharmacokinetics and Personalization. AI-based systems transcend the limitations of classical pharmacokinetic models by allowing for the dynamic modulation of infusion regimens. Through continuous monitoring, AI facilitates real-time dosage adjustments, optimizing factor consumption while ensuring maximal hemostatic protection. AI-Enhanced Joint Ultrasonography and Structural Damage Assessment The integration of AI into point-of-care ultrasound allows for a standardized and objective evaluation of joint health, even when performed at the patient’s home. Beyond detecting synovial hypertrophy and early effusions, advanced AI algorithms can accurately quantify cartilage degeneration (thinning and loss of homogeneity) and identify early subchondral bone damage (erosions and osteophytes). By automating the interpretation of these parameters, AI eliminates operator subjectivity with high precision. Digital Health and Patient Engagement. Advanced mobile apps and AI interfaces streamline the collection of Patient-Reported Outcomes (PROs). AI chatbots and virtual assistants enhance treatment adherence through smart reminders and real-time educational support, effectively reducing the psychological burden of disease management.
Results. AI-guided protocols significantly reduce Annualized Bleeding Rates (ABR) and chronic arthropathy. This improves quality of life (QoL) by increasing patient autonomy and reducing the uncertainty of unforeseen bleeding events.
Conclusions. Artificial intelligence is not a substitute for clinical judgment but rather a powerful ally in the multidisciplinary team’s decision-making process. Future perspectives include the integration of genomic data and digital biomarkers to foster a hemophilia care model that is increasingly personalized, safe, and sustainable for healthcare systems.