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Background The development of artificial intelligence (AI), machine learning (ML) and deep learning (DL) has advanced rapidly in the medical field, notably in trauma medicine. We aimed to systematically appraise the efficacy of AI, ML and DL models for predicting outcomes in trauma triage compared to conventional triage tools. Methods We searched PubMed, MEDLINE, ProQuest, Embase and reference lists for studies published from 1 January 2010 to 9 June 2022. We included studies which analysed the use of AI, ML and DL models for trauma triage in human subjects. Reviews and AI/ML/DL models used for other purposes such as teaching, or diagnosis were excluded. Data was extracted on AI/ML/DL model type, comparison tools, primary outcomes and secondary outcomes. We performed meta-analysis on studies reporting our main outcomes of mortality, hospitalisation and critical care admission. Results One hundred and fourteen studies were identified in our search, of which 14 studies were included in the systematic review and 10 were included in the meta-analysis. All studies performed external validation. The best-performing AI/ML/DL models outperformed conventional trauma triage tools for all outcomes in all studies except two. For mortality, the mean area under the receiver operating characteristic (AUROC) score difference between AI/ML/DL models and conventional trauma triage was 0.09, 95% CI (0.02, 0.15), favouring AI/ML/DL models (p = 0.008). The mean AUROC score difference for hospitalisation was 0.11, 95% CI (0.10, 0.13), favouring AI/ML/DL models (p = 0.0001). For critical care admission, the mean AUROC score difference was 0.09, 95% CI (0.08, 0.10) favouring AI/ML/DL models (p = 0.00001). Conclusions This review demonstrates that the predictive ability of AI/ML/DL models is significantly better than conventional trauma triage tools for outcomes of mortality, hospitalisation and critical care admission. However, further research and in particular randomised controlled trials are required to evaluate the clinical and economic impacts of using AI/ML/DL models in trauma medicine.

Introduction Despite trauma accounting 9% of global mortality, it has been demonstrated that undergraduate trauma teaching is inadequate nationally and worldwide. With COVID-19 exacerbating this situation, a scalable, accessible, and cost-effective undergraduate trauma teaching is required. Methods Our Continual Professional Development United Kingdom (CPUDK)-accredited University Hospitals Birmingham (UHB) Major Trauma Service (MTS) affiliated programme consisted of seven biweekly pre-recorded sessions that were delivered online through the Moodle educational platform to University of Birmingham students. Pre- and post-randomised session-specific multiple-choice questions (MCQs) and anonymous feedback forms were administered. Results There were 489 student responses, with 63 students completing all seven sessions. On an 8-point scale, students’ objective knowledge scores increased by a mean of 1.2 ( p  < 0.001). Using a 5-point Likert scale, students also showed improvement in subjective outcomes including their confidence in assessing trauma patient (absolute difference (AD) 1.38, p  < 0.001), advising initial investigations and formulating initial management plans (AD 1.78, p  < 0.001) and thereby their confidence to manage a trauma patient overall (AD 1.98, p  < 0.001). A total of 410 student responses endorsed the online delivery of SATMAS through Moodle and recommended SATMAS to future medical students. Conclusion SATMAS has demonstrated positive student feedback and extensive recruitment from only one centre, demonstrating that our programme can be an indispensable low-cost learning resource that prepares undergraduate medical students for their trauma exams and informs the implementation of clinical skills required by all doctors. We publish our pilot study findings to encourage similar teaching programmes to be adopted at other universities nationally and internationally, to synergistically benefit students, tutors, and ultimately patients, on a larger scale.