Artificial Intelligence for Predicting Secondary Complications and Clinical Outcomes in Traumatic Brain Injury: A...

Secondary complications following moderate-to-severe traumatic brain injury (TBI)-including elevated intracranial pressure (ICP), post-traumatic seizures, trauma-induced coagulopathy (TIC), and sepsis-substantially worsen patient prognosis. Current prognostic tools estimate overall mortality and disability at admission but do not predict specific, treatable complications during hospitalization. This narrative review evaluates whether AI-based prediction tools can address this unmet clinical need, appraises the maturity of evidence across five prediction domains, and identifies priorities for future research. A targeted literature search was conducted across PubMed, Embase, and Web of Science from January 2016-October 2025, supplemented by manual reference tracking. Studies were selected based on their relevance to AI or machine learning (ML) for predicting secondary complications or clinical outcomes in TBI patients. AI-based models achieve moderate predictive accuracy (area under the receiver operating characteristic curve [AUC] of 0.70-0.79) to good accuracy (AUC ≥ 0.80) for ICP crises, TIC, sepsis, and mortality. Seizure prediction has the least mature evidence, with no external validation studies. ICP prediction has the strongest evidence base, with external validation and independent replication. Mortality prediction has the largest evidence volume, with international multi-dataset validation. Critical methodological limitations persist: most models derive from retrospective, single-institution data; only approximately one-third have undergone external validation; and no randomized trials have demonstrated that AI-guided decisions improve patient outcomes. AI prediction tools show promise for forecasting secondary TBI complications, but current evidence does not support routine clinical implementation. Before adoption, these tools require rigorous external validation, prospective outcome trials, and systematic equity assessment across diverse populations.