• Tobias Gauss, Zane Perkins, and Thorsten Tjardes.
• Anesthesia and Critical Care, Grenoble Alpes, University Hospital, Grenoble, France.
• Curr Opin Crit Care. 2023 Dec 1; 29 (6): 713721713-721.

Purpose Of ReviewRecent technological advances have accelerated the use of Machine Learning in trauma science. This review provides an overview on the available evidence for research and patient care. The review aims to familiarize clinicians with this rapidly evolving field, offer perspectives, and identify existing and future challenges.Recent FindingsThe available evidence predominantly focuses on retrospective algorithm construction to predict outcomes. Few studies have explored actionable outcomes, workflow integration, or the impact on patient care. Machine Learning and data science have the potential to simplify data capture and enhance counterfactual causal inference research from observational data to address complex issues. However, regulatory, legal, and ethical challenges associated with the use of Machine Learning in trauma care deserve particular attention.SummaryMachine Learning holds promise for actionable decision support in trauma science, but rigorous proof-of-concept studies are urgently needed. Future research should assess workflow integration, human-machine interaction, and, most importantly, the impact on patient outcome. Machine Learning enhanced causal inference for observational data carries an enormous potential to change trauma research as complement to randomized studies. The scientific trauma community needs to engage with the existing challenges to drive progress in the field.Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.

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