Current knowledge and availability of machine learning across the spectrum of trauma science

Abstract

Purpose of review Recent 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 findings The 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. Summary Machine 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.

Machine-Learning Enhanced Prediction of Need for Hemorrhage Resuscitation after Trauma – The ShockMatrix Pilot Study

“…Median age was 36 years [IQR 25-52] and the cohort was mainly composed of men 79% (22 356 male, 6 062 female). Median ISS was 13 [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Table 1 provides a detailed description of the cohort.…”

“…Most studies focus on model development, few perform external validation and rarely prospective, and even fewer attempt a prospective work ow integration or real-life validation [12,13] Although a recent guideline highlights the need to consider usability, ergonomics, explicability, and human-machine interaction when dealing with decision support tools [14], a knowledge gap persists about patient or work ow impacts and real-life feasibility.…”

Editorial: Acute trauma management in a changing environment

“…However, there is still much work ahead to gain a deeper understanding of which (sub)populations may benefit best from which therapies and at which time point(s) within their journey. Gauss and colleagues conclude this issue with a first snapshot into the future on how novel technologies such as machine learning and artificial intelligence may impact and drive trauma care and science in the years to come [15].…”