Good things come in threes: evaluating clinical utility of machine learning-derived clusters

Background: Machine learning (ML)-based cluster analysis is a common method for subtyping medical conditions and presentations of disease states. Recent advancements in algorithms and increasing access to vast healthcare data have further increased the use of such ML models. However, practical implementation is lacking, largely due to methodological limitations and insufficient reporting and clinical contextualization in the extant literature, with no existing guidelines for this purpose. Objective: To propose a general framework to assess and ensure clinical utility of ML-derived clusters. Discussion: The proposed framework encompasses three domains, focusing on clinical relevance, stability and generalizability, and ease of identification, enabling researchers and readers to ensure clinical utility of ML-generated subgroups.