Making Machine Learning Clinically Useful in Thrombosis and Hemostasis: A Roadmap for Diagnostic Translation

Artificial intelligence (AI), most often in the form of machine learning (ML), attracts high expectations across medicine and is often discussed as a transformative, rapidly evolving topic. In thrombosis and hemostasis, these expectations are reinforced by the nature of clinical decision-making, which rarely hinges on a single definitive test. Instead, clinicians integrate clinical context with laboratory results, imaging, and treatment information. Despite a rapidly expanding literature, translation into routine decision support remains scarce. Most published models are early-stage prototypes and provide limited evidence for transportability, clinical utility, and safe deployment. In this review, we argue that the most practical way to achieve and assess clinical usefulness is to treat ML tools as diagnostic instruments. This framing also clarifies what "useful" means at the bedside: improving a specific decision, at a specific moment, for a specific patient. Building on this concept, we provide a roadmap for diagnostic translation, starting with intended use and population definition, then addressing real-world data and reference standards, fit-for-purpose model behavior, phased validation as evidence generation, and the requirements for implementation and lifecycle governance. Two figures summarize the translation roadmap and map ML to laboratory thinking from preanalytics to quality management. A clinically deployable checklist supports structured reading of published studies and helps identify what evidence is missing for implementation. The goal is to help clinicians and laboratory specialists distinguish promising prototypes from tools that are ready for clinical translation and sustainable use. This article is a narrative review. It does not aim to provide a systematic synthesis of all machine learning (ML) studies in the field. Our aim is to provide a practical roadmap for diagnostic translation and two pragmatic tools that help readers assess published studies and plan evidence generation toward clinically deployable ML tools. To do so, we formulated a concept-driven translation framework by combining (1) established principles from diagnostic research and test evaluation, (2) targeted searches for guidance and reporting frameworks relevant to clinical ML and diagnostic translation, and (3) selective review of systematic reviews and meta-research on common sources of bias, external validation, and implementation barriers. Case examples were chosen to illustrate how the framework applies to real tools and to highlight where translation succeeds or stalls.