Machine Learning in Emergency Medicine: Keys to Future Success.

An era for artificial intelligence has arrived for emergency medicine. In the systematic review by Kareemi et al. (Ref 1.) published in this issue of Academic Emergency Medicine, the authors evaluate the performance of machine learning (ML) models versus standard care (e.g. clinical decision rules, provider judgement) in emergency medicine across a variety of clinical scenarios and outcomes. The systematic review concludes that ML has superior performance in almost all tasks, but also calls attention to several widespread shortcomings including limited adherence to reporting guidelines and the lack of evaluation through interventional trials. These findings highlight the need for a new phase in clinical decision support (CDS) for emergency care with research and practice focused on integrated, machine learning-driven CDS systems that are usable, interpretable, and effective. In this commentary, we review key concept areas for enhancing the performance, promoting the adoption, and studying the impact of ML within emergency medicine. We also discuss the interpretation and application of machine learning studies and projects, dividing key concepts into two domains: intrinsic - elements of the model and its task-based performance - and extrinsic - the ability for the model to achieve a desired objective with respect to patient care.