Objective:

To systematically synthesize the current state meta-research evidence from existing systematic reviews and meta-analyses on the applications of artificial intelligence (AI) technologies in traumatic brain injury (TBI) research and clinical practice.  

Background:

TBI diagnosis and prognosis are complex and critical aspects of patient management, with significant variability in outcomes. In data-driven predictions, AI models have been used to analyze patient data for early detection of adverse outcomes following TBI.   

Design/Methods:

We systematically searched Scopus, Google Scholar, and additional sources. We included articles published from January 1st, 2020 to June 18th, 2025 that met the following criteria: (1) published as full-length article in peer-reviewed journals, (2) focused on the applications of any form of AI technologies in any area of TBI research or practice, (3) used one of the systematic literature review methods, and (4) full text was available in English language.  

Results:

Across the 13 systematic reviews and meta-analyses analyzed, machine learning (ML) models consistently showed high accuracy in predicting mortality, adverse outcomes, and intracranial complications following TBI. The reported AUC values varied between 0.72 and 0.96, with most studies reporting an overall accuracy above 80%. Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were the most utilized techniques and frequently surpassed traditional logistic regression models in performance. Ensemble methods like Random Forest and Gradient Boosting enhanced predictions for intracranial hypertension by as much as 20%. Significant heterogeneity in methods, measures, and outcomes were identified across the included articles.   

Conclusions:

AI and ML methods offer strong potential for improving prognostic predictions in TBI, with several algorithms achieving high diagnostic and predictive accuracy. Nonetheless, existing findings are limited due to methodological and data ethics-related issues and inadequate validation in real-world scenarios. Future studies should prioritize advancing methods and ethics of AI-based predictive analytics, alongside evidence-informed integration of AI in the clinical management of TBI.