From Uncertainty to Reality: Overcoming the Translational Gap in AI-interpreted Platelet Dynamics for Thrombotic Risk Prediction

Abstract

Artificial intelligence (AI) is rapidly transforming the landscape of thrombosis research, offering novel opportunities to enhance the understanding and prediction of platelet-driven thrombotic events. Platelets play a central role in hemostasis and pathological thrombosis, yet conventional diagnostic methods remain limited in their ability to capture the dynamic and multifactorial nature of platelet function. This study explores the emerging role of AI in orchestrating platelet dynamics for improved thrombotic risk prediction and examines the translational gap between technological innovation and clinical application. A narrative synthesis of current literature was conducted, integrating findings from studies on platelet biology, computational thrombosis modeling, machine learning, and advanced diagnostic technologies. The results demonstrate that AI-driven models, particularly those incorporating multimodal datasets such as imaging, transcriptomics, and microfluidic simulations, significantly enhance predictive accuracy compared to traditional approaches. Reinforcement learning and dynamic digital models further enable continuous risk adaptation and real-time clinical decision support. Despite these advancements, several barriers to clinical translation persist, including data heterogeneity, limited external validation, challenges in model interpretability, and ethical concerns related to data privacy and algorithmic bias. Additionally, variability in healthcare infrastructure and regulatory uncertainties hinder widespread implementation. Addressing these challenges requires standardized frameworks, robust validation strategies, and interdisciplinary collaboration to ensure seamless integration into clinical workflows. Overall, AI-orchestrated platelet dynamics represent a promising frontier in precision medicine, with the potential to revolutionise thrombotic risk prediction and patient management if the existing translational barriers can be effectively overcome.