Artificial intelligence (AI) is increasingly explored in healthcare for its capacity to analyse complex data, support clinical decision-making and enable more personalised care. In haemophilia, AI is emerging as a potential driver of transformation across the care continuum. This narrative review synthesises current evidence, early achievements, limitations and future opportunities related to AI in haemophilia, drawing on the evolving scientific literature, initial clinical applications and perspectives from patients, healthcare professionals and global organisations. To date, AI initiatives in haemophilia span multiple domains, including joint imaging and musculoskeletal assessment, bleeding risk prediction, inhibitor risk stratification, coagulation modelling, surgical support and patient education. Machine-learning and generative AI approaches show promise in improving diagnostic consistency, enabling more individualised treatment strategies and enhancing patient engagement through digital and conversational tools. Beyond direct clinical applications, AI is also being explored as an enabler of medical education, clinical workflow optimisation, health system planning, guideline implementation and future therapeutic innovation, including gene-based and novel haemostatic therapies. Despite this momentum, AI applications in haemophilia remain at an early stage. Data scarcity intrinsic to rare diseases, limited model interpretability, biological complexity, ethical concerns and the need for robust clinical validation currently limit widespread implementation. Overcoming these challenges will require high-quality standardised data, transparent and explainable models, appropriate regulatory frameworks, education of clinicians and patients and sustained multidisciplinary collaboration.
Cedric Hermans (Thu,) studied this question.