Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.

Journal: NPJ digital medicine
Published Date: Jun 18, 2025

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.

Authors

  • Armaan K Malhotra
    Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
  • Husain Shakil
    Division of Neurosurgery, Unity Health, Toronto, ON, Canada.
  • Christopher W Smith
    Department of Chemistry, University at Albany, State University of New York, Albany, New York 12222, United States.
  • Yu Qing Huang
    St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
  • Jethro C C Kwong
    Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
  • Kevin E Thorpe
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Christopher D Witiw
    Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.
  • Abhaya V Kulkarni
    Department of Surgery, Hospital for Sick Children, University of Toronto, CA, USA.
  • Jefferson R Wilson
    Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.
  • Avery B Nathens
    Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. avery.nathens@sunnybrook.ca.

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