Review Article
Here, we present a narrative review evaluating artificial intelligence (AI)-based prediction tools for secondary complications and clinical outcomes in traumatic brain injury, appraising evidence maturity across five complication domains and identifying critical barriers to clinical implementation.
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.
TBI constitutes a leading cause of death and long-term disability worldwide, with approximately 69 million incident cases occurring annually1,2. TBI is the principal cause of death and disability among individuals under 45 years of age, imposing profound healthcare, socioeconomic, and societal burdens1,2,3. A fundamental distinction in TBI pathophysiology separates primary injury—the immediate mechanical damage sustained at impact—from secondary injury, which evolves over subsequent hours to days through cascading processes including cerebral edema, ischemia, excitotoxicity, and neuroinflammation3,4. Unlike primary injury, secondary injury mechanisms are, in principle, modifiable therapeutic targets. The secondary complications arising from these processes—including elevated ICP, post-traumatic seizures, TIC, and sepsis—frequently determine the ultimate clinical trajectory of the patient3,5,6. The fundamental clinical challenge is identifying which patients will develop these complications before they manifest, thereby enabling earlier, more targeted preventive intervention.
Clinicians currently employ the GCS for consciousness assessment, CT imaging for structural injury characterization, and the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) and Corticosteroid Randomization After Significant Head Injury (CRASH) calculators for estimating six-month mortality and unfavorable outcomes7,8. While clinically valuable, these tools share important limitations: they rely exclusively on admission data, presuppose linear predictor-outcome relationships, and were designed to forecast death or long-term disability rather than specific, individually treatable complications arising during the hospitalization period6,7,8. AI—an overarching field encompassing machine learning (ML), a subset involving algorithms that learn predictive patterns from data—can interrogate complex, nonlinear relationships across large variable sets and assimilate continuously updated physiological monitoring streams5,6,7. These capabilities suggest that AI may detect subtle precursor signals of imminent deterioration that escape standard clinical assessment. The 2022 Lancet Neurology Commission on TBI explicitly identified advanced prognostic analytics as a research and clinical development priority3, a direction also advocated in the 2017 research roadmap for the field9. This narrative review examines current evidence for AI applications in predicting secondary TBI complications and clinical outcomes, evaluates the maturity of evidence across complication domains, and identifies what must be achieved before these tools can be responsibly translated into clinical practice.
Several prior systematic reviews have evaluated AI outcome prediction in TBI5,6. The systematic review by Malhotra et al.5 encompassed 39 studies focused predominantly on mortality and global disability prediction using admission-level variables. Courville et al.6 conducted a meta-analysis of ML algorithms for predicting TBI outcomes. The present review is distinguished by its specific focus on individual secondary complications (ICP crises, seizures, coagulopathy, and sepsis) as discrete, clinically actionable prediction targets during the inpatient period. By organizing the evidence around complication-specific prediction with attention to the maturity and actionability of each domain, this review provides a clinician-oriented appraisal that prior aggregate-outcome analyses have not foregrounded.
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Search strategy and study selection
A targeted, non-systematic literature search was conducted across PubMed, Embase, and Web of Science from January 2016–October 2025. The 2016 start date was chosen to capture foundational ML publications predating the recent surge in deep learning research while maintaining a contemporary focus; its selection is specifically justified by the inclusion of Myers et al. (2016)10, a seminal study that established core methodological approaches for ML-based ICP prediction. The search was supplemented by manual screening of the reference lists of identified systematic reviews and primary studies.
Search terms were combined using Boolean operators and included: “traumatic brain injury,” “TBI,” “artificial intelligence,” “machine learning,” “deep learning,” “neural network,” “intracranial pressure,” “seizure,” “coagulopathy,” “sepsis,” “ventilator-associated pneumonia,” “outcome prediction,” and “prognostication.”
Inclusion criteria:
(1) original research studies or published systematic reviews; (2) focus on AI or ML prediction of secondary complications or clinical outcomes in TBI patients; (3) reporting of at least one quantitative performance metric (e.g., AUC, sensitivity/specificity, accuracy); (4) publication in a peer-reviewed journal or indexed conference proceedings with accessible full text.
Exclusion criteria:
(1) studies employing only conventional statistical models without AI or ML components; (2) studies restricted to injury severity classification or primary injury characterization without predicting complications or outcomes; (3) preclinical (animal) or in vitro studies; (4) review articles without primary data relevant to the prediction targets of interest.
Fifteen primary studies were selected as representative examples across the five prediction domains, prioritized by methodological quality, diversity of validation approach, and clinical relevance. Two additional ICP prediction studies were identified through manual reference tracking and are included to provide broader coverage of this domain. As a narrative rather than a systematic review, selection aimed at representative coverage rather than exhaustive enumeration; selection bias cannot be fully excluded. No formal quality or risk-of-bias assessment instrument (e.g., PROBAST or TRIPOD reporting checklist) was applied to individual studies—a limitation addressed in the Limitations section.
Clinical importance of predicting secondary complications
Secondary complications after TBI are common, frequently preventable with timely intervention, and profoundly influence clinical outcomes3,5. Understanding the clinical context of each complication type clarifies why prediction tools could prove transformative in critical care practice. Elevated ICP is among the most consequential and time-critical complications following severe TBI4,10. When ICP persistently exceeds 20–22 mmHg, cerebral perfusion pressure falls and ischemic secondary injury propagates4. If clinicians could reliably anticipate ICP crises before their onset, they could proactively optimize head positioning, titrate sedation, administer osmotic therapy, or prepare surgical decompression—transitions from reactive crisis management to preventive intervention that could substantially reduce the burden of secondary ischemic injury10,11.
Early post-traumatic seizures can cause secondary injury through elevated metabolic demand, sustained excitotoxicity, and propagation of cortical spreading depolarizations. Accurate risk stratification would inform decisions regarding prophylactic anticonvulsant therapy, the threshold for continuous electroencephalographic (EEG) monitoring, and the intensity of seizure surveillance12,13. TIC affects up to 60% of patients with severe TBI and is associated with a three- to fourfold increase in mortality14,15,16. Early identification of coagulopathic patients enables targeted coagulation correction before hemorrhage worsens. Prolonged intensive care with mechanical ventilation and invasive monitoring substantially elevates infection risk, and AI-based prediction tools17,18 could enable risk-stratified infection prevention protocols to meaningfully reduce infectious complication rates. Across all these domains, accurate prediction also informs treatment intensity decisions, family communication, rehabilitation planning, and goals-of-care discussions5,7,19.
Current evidence for AI-based prediction
Research examining AI prediction in TBI has expanded considerably over the past decade5,6,7. The systematic review and meta-analysis by Courville et al. demonstrated that ML algorithms can outperform traditional logistic regression models in predicting adverse TBI outcomes, with admission GCS score, patient age, and selected serum biomarkers identified as the most consistently important predictive features6. The comprehensive systematic review by Malhotra et al., encompassing 39 studies and 592,323 patients, confirmed that the strongest predictive factors—age, GCS score, pupillary response, and CT findings—substantially overlap with features that experienced clinicians already incorporate into their reasoning5,7. When AI models are restricted to these same admission-level variables as traditional calculators, accuracy gains may be modest6. However, when given access to richer, longitudinal data streams—continuous physiological monitoring trends, serial laboratory panels, and quantitative neuroimaging—AI approaches may offer greater advantages over conventional tools. The characteristics and validation strategies of representative studies across these five domains are summarized in Table 1, and the conceptual prediction pipeline is illustrated in Figure 1.
Intracranial pressure prediction
Of all secondary complication domains, ICP prediction has accumulated the most robust evidence base. AI systems can predict dangerous ICP elevations approximately 30 min in advance, providing a potentially actionable window for preventive intervention10,11. Myers et al. established a foundational methodology using ML to predict both ICP and brain tissue oxygen partial pressure (PbtO₂) crises from continuous ICU monitoring waveforms10. Carra et al. subsequently developed and externally validated ML models for predicting harmful cumulative ICP doses using the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) dataset, demonstrating robust cross-center generalizability11. Lee et al. developed a hybrid convolutional neural network (CNN)–long short-term memory (LSTM) architecture applied to continuous ICP waveform data, achieving accurate within-patient ICP trajectory prediction20. Mataczyński et al. proposed an explainable TabNet-based model committee applied to multi-modal physiological signal-derived metrics, achieving high recall (0.94) for predicting ICP crises 30 min in advance and improving upon conventional threshold-based approaches21. This domain benefits from multiple independent replication studies, external validation, and a clear physiological rationale for the clinical actionability of early predictions.
Post-traumatic seizure prediction
For post-traumatic seizures, studies employing neuroimaging and EEG-based features have demonstrated moderate discriminative performance in identifying high-risk patients12,13. Garner et al. applied a random forest classifier to resting-state functional magnetic resonance imaging (fMRI) connectivity data in TBI patients, achieving 69% raw accuracy in cross-validation for predicting seizure susceptibility12. Faghihpirayesh et al. applied deep learning to EEG data to detect epileptiform abnormalities in acute TBI, reporting a sensitivity of 0.78 and a specificity of 0.8413. These results require critical methodological appraisal: early post-traumatic seizures occur in approximately 4–15% of patients with severe TBI22,23, creating substantial class imbalance in prediction datasets. In this context, 69% accuracy is not meaningfully superior to a naive classifier that predicts the negative class for all observations; AUC and area under the precision-recall curve constitute far more informative performance measures for such imbalanced clinical prediction problems, and neither was reported by Garner et al. Notably, both studies in this domain were published as indexed conference proceedings rather than full-length peer-reviewed journal articles, further underscoring the nascent state of this evidence base. Seizure prediction currently represents the least mature evidence domain, with no published external validation studies, limited sample sizes, and performance metrics that warrant cautious interpretation.
TIC Prediction
For TIC, Yang et al. developed ML models employing XGBoost, random forest, and logistic regression classifiers, achieving AUC values of 0.82–0.85 on internal validation in a TBI-specific cohort24. Li K et al. independently developed an ML ensemble model for acute traumatic coagulopathy prediction in emergency-admitted trauma patients, reporting an AUC of 0.8925. These complementary studies demonstrate consistent performance profiles across partially overlapping patient populations. Xiong et al. employed ten cross-center models across 13,235 general trauma patients (including, but not limited to, TBI cases) and demonstrated consistent external validation performance across four independent institutions26; however, the generalizability of these findings specifically to TBI-related coagulopathy warrants further evaluation in dedicated TBI cohorts. Coagulopathy prediction has moderate evidence maturity: internal validation performance is consistent across studies, but TBI-specific external validation—particularly for the highly lethal coagulopathic phenotype that affects up to 60% of severe TBI patients and increases mortality three- to fourfold15,16—remains an unfilled gap.
Sepsis and infection prediction
Liu et al. developed an explainable XGBoost-based model specifically designed for TBI-related sepsis prediction, achieving an AUC of 0.81 on internal validation and 0.76 on external validation using the eICU Collaborative Research Database17. Shapley additive explanations (SHAP) values provided transparent feature attribution, identifying key sepsis predictors, including inflammatory biomarkers and early clinical deterioration signals. Hu et al. developed and internally validated ML models—encompassing logistic regression, random forest, and XGBoost—for stratifying 30-day sepsis risk at ICU admission in TBI patients, achieving an AUC of 0.7918. Li et al. developed a real-time XGBoost model for sepsis prediction using hourly MIMIC-IV data in trauma patients, with temporal validation confirming sustained predictive performance over the ICU course27. Wang et al. demonstrated effective ventilator-associated pneumonia (VAP) prediction in TBI patients using ensemble methods applied to MIMIC-III, achieving an AUC of 0.8728. The presence of an externally validated model (Liu et al.) distinguishes the sepsis prediction literature; however, all four studies relied on retrospective administrative or registry databases rather than prospective patient cohorts, limiting the generalizability of reported performance estimates.
Mortality and functional outcome prediction
Mortality and functional outcome prediction has the largest and most extensively validated evidence base among the domains reviewed. Pease et al. developed a deep learning model utilizing head CT scans that achieved an AUC of 0.92 for mortality prediction on internal validation, exceeding both the IMPACT model and neurosurgeon performance29. On external validation using the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) dataset, model performance decreased to an AUC of 0.80—a level comparable to the IMPACT model—underscoring the well-recognized risk of optimistic internal validation estimates29. Hibi et al. developed a multimodal ML prognostication model integrating clinical data and quantitative CT imaging using both the CENTER-TBI and the Comparative Indian Neurotrauma Effectiveness Research in Traumatic Brain Injury (CINTER-TBI) datasets, demonstrating that multimodal data fusion improved prognostication accuracy beyond unimodal approaches30. Warman et al. compared ML mortality prediction performance across high-income countries (HIC) and low- and middle-income countries (LMIC), constituting a cross-population generalizability analysis rather than standard internal or external validation; performance degradation in LMIC settings highlighted important equity-related limitations31. Raj et al. developed dynamic mortality prediction models using logistic regression applied to evolving ICU physiological data, with AUC improving from 0.67–0.72 on day 1 to 0.81–0.84 by day 5 of monitoring, demonstrating superior performance relative to static admission-based models32. These studies collectively represent the most developed evidence base for AI prediction in TBI, with multiple successful international external validations.
Critical appraisal and implementation barriers
Predictive discrimination versus clinical utility
A fundamental distinction—frequently conflated in the published literature—separates predictive discrimination from evidence of clinical utility. Predictive discrimination, quantified by AUC, measures a model’s capacity to rank patients by risk; it does not determine whether acting on those predictions improves clinical outcomes. For responsible clinical implementation, a complete evidentiary chain is required: (1) calibration, confirming that predicted probabilities align with observed event rates; (2) decision-curve analysis (DCA) demonstrating net clinical benefit across a range of decision thresholds; and (3) prospective—ideally randomized—evidence that algorithm-guided clinical interventions translate into improved patient-centered outcomes. No published TBI AI prediction study has yet completed this chain of evidence, representing the most critical evidentiary gap in the field.
Validation and methodological rigor
The AUC values reported across the reviewed studies must be interpreted with awareness of substantial methodological limitations5,33. Most published models lack external validation on independent datasets; among the 39 studies reviewed by Malhotra et al., only approximately one-third underwent external validation5. Application of the APPRAISE-AI quantitative appraisal tool revealed that methodological conduct (median score 35%), robustness of results (20%), and reproducibility (35%) were the lowest-scoring domains in this literature5. No randomized trials have demonstrated that AI-guided clinical decisions improve patient outcomes—a critical evidentiary gap that cannot be bridged by retrospective accuracy metrics alone.
Calibration, class imbalance, and baseline comparators
Beyond discrimination, several additional methodological deficiencies limit the interpretability of published findings. Calibration is rarely assessed or reported; a well-discriminating but poorly calibrated model may systematically misrepresent absolute risk, potentially misleading clinical decisions. Class imbalance is a pervasive challenge in complication prediction: rare events such as post-traumatic seizures create datasets dominated by negative observations, inflating accuracy metrics without providing meaningful predictive utility. Strategies for addressing class imbalance—including oversampling, cost-sensitive learning, and decision threshold adjustment—are inconsistently reported. Comparative evaluation against strong logistic regression baselines is frequently omitted, making it difficult to determine whether performance gains reflect genuine ML-specific advantages or simply adequate feature engineering. DCA demonstrates a net clinical benefit in fewer than one-fifth of reviewed studies.
Interpretability
Many high-performing AI models function as black boxes, providing predictions without an interpretable rationale5,6,33,34. London examined the theoretical tradeoffs between accuracy and explainability in medical AI, noting that opaque decision-making is already common in medicine and that explainability expectations may need calibration33. Hassija et al. provided a comprehensive technical review of explainable AI methodologies applicable to clinical settings35. Ghassemi et al. offered a counterpoint, arguing that current explainable AI approaches may provide false reassurance about safety and accountability without materially improving patient-level decision quality36. Clinicians reasonably hesitate to trust outputs they cannot independently evaluate, and the medicolegal implications of AI-influenced decisions remain unresolved in most jurisdictions.
Infrastructure, integration, and alert fatigue
Practical deployment requires computational infrastructure, data integration pipelines, and system maintenance capabilities that many institutions—particularly in lower-resource settings—do not currently possess5. Real-time prediction systems must be carefully calibrated to balance sensitivity against specificity to avoid alarm fatigue, a well-documented threat to patient safety in intensive care environments. Integration with existing electronic health record platforms presents substantial interoperability, latency, and workflow-disruption challenges that are rarely addressed in published algorithm development studies.
Equity and generalizability
AI systems trained on non-representative datasets may perform unequally—and potentially harmfully—across patient subgroups5,31. Most reviewed models were developed using data from high-income academic medical centers, creating a risk of geographic, institutional, and demographic performance bias5. Warman et al. explicitly addressed this concern, demonstrating measurable performance degradation when HIC-trained models were applied in LMIC settings31. No reviewed study formally assessed model performance across demographic subgroups defined by age, sex, race, or ethnicity—a foundational omission given the diverse populations served by TBI trauma centers worldwide.
Comparative evidence maturity across prediction domains
The five prediction domains reviewed differ substantially in evidence maturity. ICP prediction has the strongest foundation: it benefits from foundational studies, external validation using CENTER-TBI data, independent replication across multiple groups, and a clearly articulated, physiologically plausible clinical workflow for acting on predictions. Mortality and functional outcome prediction have the largest published literature, with multiple international external validation studies using TRACK-TBI, CENTER-TBI, and CINTER-TBI datasets. Coagulopathy prediction demonstrates consistent internal validation performance, but TBI-specific external validation remains absent from the literature. Sepsis prediction has at least one externally validated model (Liu et al.), but reliance on administrative databases limits confidence in the generalizability of performance. Seizure prediction has the least mature evidence base: no external validation exists, the available studies are small, and class imbalance substantially limits the interpretability of reported performance metrics. This differential maturity has direct implications for prioritizing research investment and for the caution warranted in any future clinical translation discussion.
Future directions and research priorities
Rigorous, pre-registered external validation across diverse clinical environments constitutes the most urgent research priority5,6. Multi-institutional frameworks such as CENTER-TBI and TRACK-TBI provide established templates and infrastructure for such validation29,30. Prospective studies, including randomized platform trials with AI-guided intervention arms, are needed to determine whether these tools meaningfully improve patient outcomes beyond standard care5. Development of AI systems that provide clinically interpretable, case-specific explanations would substantially enhance clinician trust, identify failure modes, and facilitate regulatory approval5,6,33,34. Methodological improvements—standardized calibration assessment, DCA as a required performance metric, transparent class-imbalance handling, and comparison against strong clinical baselines—should become minimum reporting standards. Studies specifically designed to assess predictive performance across demographic and geographic subgroups are necessary before any broad clinical recommendation can be made5,31. Adoption of the TRIPOD reporting standard for prediction model transparency and the PROBAST risk-of-bias assessment tool should be mandated by journals publishing in this space to improve reproducibility and comparability.
Ethical considerations
The integration of AI prediction tools into critical care medicine raises ethical considerations that extend well beyond technical performance metrics5,33. Equity concerns are central: models trained on non-representative patient populations may systematically disadvantage patients from underrepresented groups, potentially widening rather than narrowing existing disparities in TBI outcomes. Responsible tool development requires deliberate training, data diversification, and mandates subgroup performance assessment before any deployment. Transparency and accountability for AI-influenced clinical decisions remain unresolved: when an AI recommendation contributes to an adverse outcome, questions of liability and informed consent are not yet adequately addressed in existing legal or regulatory frameworks33,34. Patient and family engagement in AI deployment decisions and clear communication of the probabilistic and uncertain nature of AI predictions represent additional ethical obligations for implementing institutions.
Limitations of this review
Several limitations of this review warrant explicit acknowledgment. First, as a narrative rather than systematic review, formal literature search protocols, PRISMA flow documentation, and exhaustive study enumeration were not employed; study selection was guided by representative coverage, and selection bias cannot be excluded. Second, no formal quality or risk-of-bias assessment instrument—such as PROBAST or the TRIPOD reporting checklist—was applied to the individual studies reviewed, limiting the confidence with which conclusions about evidence quality can be drawn. Third, the rapidly evolving literature in this area means that studies published after the October 2025 search cutoff may address some of the identified limitations. Fourth, publication bias likely inflates the apparent accuracy of AI models in the retained literature, as studies with poor predictive performance are systematically less likely to be published. Fifth, heterogeneity in outcome definitions, patient case-mix, data sources, and performance metrics across studies limits direct cross-study comparisons and meta-analytic synthesis.
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AI prediction tools demonstrate genuine potential for anticipating secondary TBI complications before their clinical emergence5,6,7. Representative studies across five complication domains report predictive accuracy ranging from moderate (AUC 0.70–0.79) to good (AUC ≥ 0.80), with the highest-performing models achieving AUC values up to 0.92 on internal validation (external validation AUC typically 0.76–0.80)5
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The authors declare no conflicts of interest. No commercial entity had any role in study design, interpretation, or the decision to submit for publication.
The authors received no specific funding for this work.
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