Machine learning in the coagulation and hemostasis arena: an overview and evaluation of methods, review of literature, and future directions

Artificial Intelligence and machine-learning (ML) studies are increasingly populating the life science space and some have also started to integrate certain clinical decision support tasks. However, most of the activities within this space understandably remain within the investigational domain and are not yet ready for broad use in healthcare. In short, artificial intelligence/ML is still in an infancy stage within the healthcare arena, and we are nowhere near reaching its full potential. Various factors have contributed to this slow adoption rate within healthcare, which include but are not limited to data accessibility and integrity issues, paucity of specialized data science personnel, certain regulatory measures, and various voids within the ML operational platform domain. However, these obstacles and voids have also introduced us to certain opportunities to better understand this arena as we fully embark on this new journey, which undoubtedly will become a major part of our future patient care activities. Considering the aforementioned needs, this review will be concentrating on various ML studies within the coagulation and hemostasis space to better understand their shared study needs, findings, and limitations. However, the ML needs within this subspecialty of medicine are not unique and most of these needs, voids, and limitations also apply to the other medical disciplines. Therefore, this review will not only concentrate on introducing the audience to ML concepts and ML study design elements but also on where the future within this arena in medicine is leading us.