Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review
Armaan K. Malhotra, Husain Shakil, Christopher W. Smith, Yu Qing Huang, Jethro C.C. Kwong, Kevin E. Thorpe, Christopher D. Witiw, Abhaya V. Kulkarni, Jefferson R. Wilson, Avery B. Nathens
Abstract
Methodological standards of existing clinical AI research remain poorly characterized and may partially explain the implementation gap between model development and meaningful clinical translation. This systematic review aims to identify AI-based methods to predict outcomes after moderate to severe traumatic brain injury (TBI), where prognostic uncertainty is highest. The APPRAISE-AI quantitative appraisal tool was used to evaluate methodological quality. We identified 39 studies comprising 592,323 patients with moderate to severe TBI. The weakest domains were methodological conduct (median score 35%), robustness of results (20%), and reproducibility (35%). Higher journal impact factor, larger sample size, more recent publication year and use of data collected in high-income countries were associated with higher APPRAISE-AI scores. Most models were trained or validated using patient populations from high-income countries, underscoring the lack of diverse development datasets and possible generalizability concerns applying models outside these settings. Given its recent development, the APPRAISE-AI tool requires ongoing measurement property assessment.