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Updated: Jun 24, 2026
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Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
Published on: May 27, 2022
Artificial Intelligence for Predicting Secondary Complications and Clinical Outcomes in Traumatic Brain Injury: A
Journal of Visualized Experiments : Jove
|June 22, 2026
Summary
Artificial intelligence (AI) shows promise for predicting secondary complications after traumatic brain injury (TBI). However, current AI tools require more validation before clinical use to improve patient outcomes.
Area of Science:
- Neuroscience and Artificial Intelligence
- Clinical Prognostics and Machine Learning
Background:
- Secondary complications (e.g., elevated intracranial pressure, seizures, coagulopathy, sepsis) significantly worsen outcomes in moderate-to-severe traumatic brain injury (TBI).
- Existing prognostic tools lack the ability to predict specific, treatable complications during hospitalization.
Purpose of the Study:
- To evaluate the potential of AI-based prediction tools in addressing the unmet clinical need for forecasting secondary TBI complications.
- To appraise the evidence maturity for AI in predicting five key TBI complication domains and identify future research priorities.
Main Methods:
- A narrative review of studies from January 2016 to October 2025, identified via PubMed, Embase, and Web of Science, focusing on AI/ML for predicting TBI complications.
- Selection criteria prioritized studies relevant to AI or machine learning (ML) in predicting secondary complications or clinical outcomes in TBI patients.
- Literature search supplemented by manual reference tracking.
Main Results:
- AI models demonstrate moderate to good predictive accuracy (AUC 0.70-0.80+) for intracranial pressure (ICP) crises, trauma-induced coagulopathy (TIC), sepsis, and mortality.
- Seizure prediction evidence is least mature, lacking external validation; ICP prediction shows the strongest evidence with external validation and replication.
- Mortality prediction has the largest evidence volume, with international multi-dataset validation, but critical limitations persist, including reliance on retrospective data and lack of prospective outcome trials.
Conclusions:
- AI prediction tools offer potential for forecasting secondary TBI complications but are not yet ready for routine clinical implementation.
- Rigorous external validation, prospective outcome trials, and systematic equity assessments are necessary before widespread adoption of AI in TBI care.
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