INTRODUCTION
Neurocritical care is defined by time-sensitive decisions in which minutes to hours can separate recoverable secondary injury from irreversible neurological disability. Despite continuous bedside monitoring, clinically important deterioration is still often recognized late—after a major event (e.g., sustained intracranial hypertension, status epilepticus, or herniation) has already declared itself. Early warning signals may be subtle and distributed across multiple streams : gradual changes in arterial blood pressure (ABP)-intracranial pressure (ICP) coupling and other waveform dynamics can precede overt ICP crises, motivating computational synthesis of high-frequency physiologic data [1]. From a pediatric neurosurgical perspective, these delays matter because they directly intersect with escalation decisions such as repeat imaging, invasive monitoring, and urgent procedural intervention (e.g., external ventricular drainage or other cerebrospinal fluid diversion) [8,15,27].
Over the past two decades, neurocritical care has expanded from intermittent assessments to increasingly layered multimodal monitoring, including hemodynamic waveforms, invasive ICP, continuous electroencephalography (EEG), and adjunctive cerebral oximetry or processed EEG indices. Yet the limiting step is often not data availability but consistent synthesis : alarm burden from monitoring and clinical decision support can obscure actionable signals, and expert-dependent interpretation (e.g., EEG) remains time- and resource-intensive [13,20,24]. These realities create an operational bottleneck at the bedside, particularly in pediatric units where experience and staffing can vary.
Artificial intelligence (AI) and machine learning (ML) offer a pragmatic approach to this bottleneck by integrating multimodal inputs, capturing temporal dynamics, and outputting continuously updated, probabilistic risk estimates that can support monitoring, early warning, and prognostication [1,16,24,28]. In pediatric and neonatal populations, smaller case volumes and developmental physiology further amplify uncertainty, strengthening the rationale for calibrated, validated decision support within strong ethical and governance frameworks [4,25,26]. However, safe translation requires more than high discrimination : local validation, calibration, workflow integration, and adherence to clinical evaluation and reporting standards are increasingly emphasized [3,6,7,18,30].
Neonatal neurocritical care and neurocritical care in older infants and children differ in developmental physiology, monitoring practice, and outcome endpoints; therefore, the evidence base for AI applications and their limitations also differs by age group. Throughout this review, we specify whether evidence derives from neonatal versus older pediatric cohorts and highlight age-specific considerations for translation.
This review summarizes clinically actionable applications of multimodal biosignal analysis for monitoring, early warning, and prognostication in neurocritical care, with an emphasis on pediatric and neonatal cohorts. We highlight where current evidence is strongest, how these tools intersect with pediatric neurosurgical decision-making, and what is required for safe translation—including external validation, calibration, workflow integration, and governance. Fig. 1 contrasts conventional monitoring workflows with an AI-enabled multimodal decision-support pipeline, and representative applications are summarized in Table 1.
MULTIMODAL BIOSIGNALS AND DATA STREAMS
Neurocritical AI applications typically combine four complementary data domains : 1) continuous physiologic waveforms, 2) neuromonitoring biosignals, 3) neuroimaging, and 4) electronic health record (EHR) context [1,2,16,17,20,22,24,27,32]. Each domain captures a different aspect of brain-body physiology; clinically useful systems often perform modality-specific preprocessing before integrating information at the decision level.
Multimodal fusion is not automatically beneficial. Biosignals contain artifacts and missingness that vary across devices and institutions, and models that work well in one unit may behave differently elsewhere when monitoring hardware, data quality, and treatment protocols differ [1,2,9,16]. Robust preprocessing, clear outcome definitions, and calibration monitoring are therefore prerequisites for responsible deployment [3,6,16,30].
Continuous physiological waveforms
Continuous waveforms (heart rate, ABP, oxygen saturation, end-tidal CO₂) provide high-frequency information on systemic physiology and its coupling to cerebral perfusion. Compared with intermittently charted values, waveform-derived features can quantify variability, trend, and cross-signal relationships (e.g., ABP-ICP coupling), which may represent early physiology of impending decompensation [1,27]. However, artifacts, missingness, and timestamp misalignment are common; signal quality control and harmonized preprocessing are prerequisites for reliable deployment [1,32].
Neuromonitoring biosignals
Brain-specific monitoring includes ICP waveforms, continuous EEG (cEEG), and cerebral oximetry (e.g., regional cerebral oxygen saturation [rcSO2] using near-infrared spectroscopy [NIRS]). These modalities are clinically meaningful but interpretation is demanding : ICP crises are dynamic, EEG requires expert review and is frequently confounded by artifacts and sedation, and NIRS signals can be influenced by probe position and extracranial contamination [1,2,20,24]. AI is well-suited to these biosignals because it can standardize pattern recognition, provide continuous quantitative trends, and potentially reduce interobserver variability in expert-dependent tests [20,24].
Neuroimaging and radiomics
Computed tomography (CT) and magnetic resonance imaging (MRI) provide structural information that complements physiology. AI applications range from computer vision for detection/segmentation to radiomics, where quantitative image features are extracted and linked to functional outcomes. In pediatric settings, imaging-based prognostication has been evaluated for neonatal hypoxic-ischemic encephalopathy (HIE) and pediatric traumatic brain injury (TBI), and clinician-facing imaging decision support has been explored in emergency care [14,15,17]. Emerging work in pediatric neuro-oncology additionally highlights AI-assisted segmentation and prognosis, as well as imaging predictors of long-term cognitive outcomes [5,10].
EHR context and interoperability
EHR data provide clinical context that biosignals alone cannot capture, including neurologic examination, laboratory values, medications (sedation, osmotherapy, antiseizure therapies), procedures, and comorbidities. Interoperable deployment requires standardized data access and representation. Here, “interoperable models” refers to prediction models designed for use across different EHR systems and institutions with minimal site-specific re-engineering through standardized variable definitions and portable execution workflows. Recent pediatric intensive care unit (ICU) studies have packaged risk models in interoperable formats and evaluated external validation and recalibration, supporting the feasibility of real-time, workflow-integrated prediction [16,22].
AI FOR MONITORING AND EARLY WARNING
Intracranial hypertension and ICP-related decision support
In critically ill children, features derived from ABP and ICP waveforms can predict impending episodes of elevated ICP. In one study of 55 critically ill children, investigators analyzed 21 elevated ICP events (from nine patients) and sampled 200 nonevent control periods without events (from 22 patients) [1]. An XGBoost model using features derived from ABP and ICP waveforms predicted impending ICP elevation 30-60 minutes in advance (area under the receiver operating characteristic curve [AUROC], 0.82; area under the precision-recall curve, 0.24), and ABP-derived features remained informative up to 240 minutes before the event. Such a multi-hour horizon is clinically meaningful because it can enable earlier escalation (hyperosmolar therapy, ventilation optimization, sedation, positioning, and timely neurosurgical consultation) before secondary injury progresses.
The prediction horizon should be explicitly reported because model performance and clinical usefulness vary by time window. In waveform-based ICP prediction, physiologic precursors strengthened as events approached, with different feature contributions across horizons; a shorter horizon (minutes) may support operational awareness, whereas a longer horizon (hours) can enable preemptive interventions and resource mobilization [1].
A key bedside design question is not only whether a model predicts events, but whether its outputs change care in a beneficial way. Prospective, clinician-facing evaluations should test whether alerts lead to earlier recognition and intervention while keeping alert burden (including false alarms) acceptable and improving patient-centered outcomes—not AUROC alone [12,30]. In a non-neuro inpatient setting, an AI-enabled deterioration detection intervention was associated with a reduction in a composite deterioration outcome around an alert threshold, highlighting that impact depends on coupling predictions to a clear response workflow [12].
AI can also support upstream decisions about invasive monitoring. A prospective cohort study in pediatric TBI developed time-updated models to predict ICP-monitor placement decisions, supporting the concept of temporally aligned decision support that updates as new data arrive [27]. Such tools may help reduce underuse in high-risk patients and overuse in low-risk patients, but clinical utility depends on transparent explanations and integration into local protocols.
cEEG : background trends and seizure detection
cEEG is central to neurocritical monitoring but is resource-intensive and subject to inter-reader variability. In neonates with perinatal asphyxia treated with therapeutic hypothermia, an automated EEG trend metric (brain state of the newborn) predicted long-term neurological outcome with AUROCs reported in the 0.95-0.99 range, with peak performance around 12 hours after birth [20]. This illustrates how automated EEG background quantification can convert expert-dependent interpretation into a standardized, continuously updated biomarker.
Automated EEG tools in practice must contend with artifacts, evolving background under sedation and hypothermia, and differences in EEG montages and devices across institutions; accordingly, external validation across sites and prospective evaluation in real-world workflows remain essential for translation [20,24].
For seizure recognition, bedside algorithms are increasingly supported by prospective evidence. In the multicenter randomized Algorithm for Neonatal Seizure Recognition (ANSeR) trial, real-time seizure-probability display improved bedside seizure recognition : clinicians correctly identified 66.0% of seizure-positive EEG hours with algorithm assistance versus 45.3% with conventional review (absolute improvement, 20.8 percentage points) [24]. Here, “seizure-positive EEG hours” refers to 1-hour EEG epochs containing electrographic seizures. The false detection rate on the bedside seizure record form—suspected seizure hours later adjudicated seizure-free by EEG experts—was similar between groups (39.3% vs. 37.4%) [24]. At the neonate level (i.e., clinician judgment that a neonate had seizures), false detection remained substantial (36.6% vs. 22.7%), underscoring why operational metrics (alert rate per patient-day, time-to-recognition, and downstream treatment effects) should be reported alongside diagnostic performance [24,30].
Multimodal physiologic fusion and non-invasive injury detection
Combining multiple non-invasive biosignals can outperform single-modality monitoring. In neonatal encephalopathy, a long short-term memory model integrating heart rate, oxygen saturation, rcSO2, and blood pressure streams achieved 91.2% accuracy at 48 hours for classifying moderate/severe versus no/mild MRI brain injury [32]. This approach is attractive because it leverages routinely available bedside monitoring and yields a risk estimate that can update as physiology evolves.
By contrast, models using rcSO2 patterns alone have shown more modest discrimination for adverse MRI outcome or death (AUROC, 0.73), supporting the pragmatic principle that individual physiologic modalities may be noisy while their joint temporal pattern can be informative [2].
Multimodal fusion raises additional implementation considerations: missing data in one modality should not invalidate the overall estimate; models should communicate uncertainty when inputs are incomplete; and dashboards must present trends in a way that supports rapid decision-making without exacerbating alarm fatigue [13,16,32].
Adult neurocritical care and adjacent acute neuro workflows provide practical precedents for real-world AI deployment. In adults, AI-enabled neuroimaging triage has been widely adopted in acute ischemic stroke, including automated CT perfusion/angiography analysis to estimate infarct core/penumbra and detect large-vessel occlusion, supporting time-critical activation and transfer pathways [23]. Deep-learning tools have also been applied to head CT to rapidly flag acute intracranial hemorrhage and other critical findings for prioritization and notification, which can accelerate neurosurgical and neurocritical responses [29]. While these adult examples often target imaging triage rather than bedside physiologic monitoring, they illustrate the central importance of workflow integration, humanin-the-loop confirmation, and ongoing performance monitoring—lessons directly applicable to pediatric translation.
AI FOR PROGNOSIS AND OUTCOME PREDICTION
ICU-level morbidity and mortality prediction
Risk stratification models based on routinely collected ICU data have progressed from proof-of-concept to externally validated and interoperable tools [16,22]. In a secondary analysis of the TOPICC study (suspected acute neurologic injury in the pediatric ICU), an ensemble model predicted mortality using admission physiology with AUROC 0.91 and average precision 0.59, outperforming conventional baselines for identifying high-risk patients [22]. Explainability analyses highlighted clinically intuitive contributors such as pupillary reactivity and Glasgow coma scale, which can improve face validity for bedside adoption [22].
From a clinical perspective, mortality prediction is only one endpoint. Patient-centered outcomes include acquired neurologic morbidity and functional disability, which inform rehabilitation planning and family counseling [16,22].
An interoperable model for identifying critically ill children at risk of new neurologic morbidity achieved AUROC 0.81 on external validation [16]. From a bedside perspective, the number needed to alert was 4—meaning that, on average, one true morbidity case was flagged for every four alerts (approximately one in four alerts was a true positive) [16]. The predicted probabilities became more reliable after simple site-level recalibration (i.e., adjusting the model’s risk estimates to match local patient mix and event rates), underscoring the importance of calibration when models are transported across institutions [16,28].
Neonatal HIE
Neonatal HIE is a leading domain for pediatric AI-based neuroprognostication because standardized imaging and meaningful developmental endpoints enable quantitative modeling [17,20]. Using MRI-derived quantitative measures, imagingonly models showed stronger correlations with 18-month Bayley-III outcomes than demographic and laboratory features alone, and combined imaging-plus-clinical models achieved correlations up to approximately r=0.67 with R2 up to 0.429, depending on the outcome domain [17]. These results suggest that quantitative imaging features can capture prognostic information beyond conventional qualitative reads, while highlighting the importance of external validation and careful interpretation.
For HIE, a key practical question is how early and how reliably prognosis can be estimated without letting an overly pessimistic early prediction influence treatment intensity and thereby affect the outcome itself. Automated EEG trends provided high discrimination for long-term outcome prediction early after birth, with peak performance around 12 hours, whereas MRI-based models are typically applied after imaging acquisition [17,20]. Accordingly, AI outputs should be interpreted as probabilistic decision support rather than deterministic labels and they should be presented with uncertainty and clinical context.
Pediatric TBI
In pediatric TBI, ML models can outperform conventional CT-based scoring systems for functional outcome prediction. A neural network model predicted 6-month outcomes with an AUROC of 0.946±0.042, exceeding Helsinki, Rotterdam, and Marshall CT classifications in the same cohort [14].
EHR-based approaches have also shown promise for mortality prediction in pediatric TBI, but these models are vulnerable to practice variation and performance changes when applied to different hospitals or time periods and therefore require rigorous external validation, recalibration, and monitoring before deployment [11,16].
Beyond prognostication, AI can influence imaging decisions. In a simulation study of pediatric head trauma, emergency physicians changed CT ordering decisions in nearly half of cases when their initial judgment disagreed with a deep-learning recommendation [15]. This design helps bridge the gap between algorithm performance and clinical impact by demonstrating an effect on clinician decision-making.
Emerging pediatric decision-support applications
AI applications are expanding toward neurosurgical and other high-risk subpopulations. For neonatal hydrocephalus, a multimodal model integrating MRI features and clinical data predicted CSF diversion-related outcomes with AUROC around 0.82, including external validation in an independent cohort [8]. In pediatric extracorporeal membrane oxygenation, an ML model demonstrated high internal performance with AUROC decreasing from 0.912 internally to 0.807 on external validation [9].
TRANSLATION TO CLINICAL PRACTICE
Even before AI tools are deployed, many neurocritical units are building the infrastructure that underpins multimodal analytics, including high-frequency waveform export, secure server-based archiving, and synchronized visualization. Fig. 2 illustrates an example of this workflow in pediatric neurocritical care.
In our institution, continuous ABP and ICP waveform archiving has enabled routine review of synchronized signals and automated trending of derived indices such as the pressure reactivity index (PRx). Importantly, these trends are used in day-to-day clinical practice to individualize cerebral perfusion pressure (CPP) targets and to support decisions about the timing of escalation, including surgical interventions, when PRx deteriorates in conjunction with evolving physiology even before conventional alarm thresholds are persistently exceeded. Beyond PRx, we are actively curating multiple continuous data streams (e.g., ECG, SpO₂, ABP/ICP-derived features, and CPP trajectories) with focused efforts on artifact reduction, noise handling, and time alignment to create analysis-ready datasets. Building on this foundation, our ongoing work aims to 1) leverage curated continuous signals for prognostic modeling and 2) develop a clinician-in-the-loop early-warning/alarm framework that prioritizes high-risk patterns while minimizing false alarms. At present, these analytics are used as decision-support tools rather than fully automated alerting systems; local validation and prospective impact evaluation are prerequisites before implementing published ML models as real-time bedside alerts.
Across applications, the main barrier to clinical impact is rarely discrimination alone. Safe translation generally follows a pragmatic sequence : 1) build reliable real-time data pipelines with signal-quality control and synchronization; 2) define the clinical target, prediction horizon, and an explicit response workflow; 3) perform external validation and site-level recalibration when models are transported; 4) deploy clinician-facing outputs with tiered alerts to minimize alarm fatigue and measure operational metrics (e.g., alert rate, number needed to alert, and time-to-recognition); and 5) continuously monitor calibration and model drift—time-related changes in patient mix, devices, or practice patterns that can degrade performance—with governance for updates [9,12,16,24,28,30].
Ethics, bias mitigation, and governance are not optional add-ons in pediatric care. Pediatric-focused frameworks emphasize developmentally appropriate consent/assent where relevant, stakeholder engagement, and routine checks to identify and reduce bias (e.g., performance differences across age groups, diagnoses, or other subgroups) throughout the AI lifecycle [4,25,26]. Governance structures should define accountability, ensure cybersecurity and data privacy, and establish escalation processes for detected model drift or safety concerns, particularly in pediatric care [4,26,28].
FUTURE PEDIATRIC APPLICATIONS AND RESEARCH PRIORITIES
Pediatric translation introduces distinct constraints—smaller datasets, broader physiologic heterogeneity across developmental stages, and outcomes that emphasize long-term neurodevelopment rather than short-term survival. To move beyond single-center studies, future pediatric neurocritical AI should prioritize multi-institutional collaboration, standardized data capture for waveforms and EEG, and transparent reporting of external validation and calibration [6,7,16,18,22,30].
Because transportability and calibration remain recurring challenges, multisite development and validation—ideally with privacy-preserving collaboration frameworks—may improve generalizability while respecting governance constraints [9,16,26,28]. At the bedside, real-time early warning will require low-latency deployment, robust data pipelines, and explicit monitoring for model drift and safety signals [28].
Prospective impact studies are the critical missing step. Trials should test whether AI-guided monitoring reduces time-to-recognition and time-to-intervention for seizures or ICP crises, decreases complications, or improves functional outcomes, while quantifying workflow burden and unintended consequences [12,24,28].
Beyond the domains summarized in Table 1, literature is emerging in several pediatric areas that remain highly relevant to neurocritical care but are underrepresented in prospective validation studies. In Moyamoya disease, systematic review evidence suggests that AI models have been applied to estimate stroke events and radiologic surrogates, but study quality is heterogeneous and most data remain retrospective [31]. For epilepsy beyond the neonatal period, a systematic review and meta-analysis of prediction models in children and adolescents reported substantial methodological heterogeneity and emphasized the need for external validation and transparent reporting before clinical adoption [19]. Ethical discussions specific to pediatric epilepsy detection further highlight welfare considerations, data governance, and bias mitigation when deploying clinician-facing algorithms [21]. In pediatric neuro-oncology, imaging-based work has explored radiologic predictors of long-term cognitive outcomes and AI-powered segmentation/prognosis approaches even under missing MRI conditions, but standardized endpoints and prospective workflow-integrated evaluations remain limited [5,10].
More broadly, the field should adopt reporting and evaluation standards, including the CONSORT-AI extension (Consolidated Standards of Reporting Trials for AI interventions), SPIRIT-AI extension (Standard Protocol Items: Recommendations for Interventional Trials for AI interventions), DECIDE-AI (early-stage clinical evaluation of AI decision support systems), and TRIPOD+AI (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis, AI extension), and implementation frameworks that explicitly address calibration, subgroup performance, and workflow impact (e.g., alert burden, alert fatigue, and clinician response) [3,6,7,12,13,18,28,30].
CONCLUSION
AI-enabled multimodal biosignal analysis is increasingly capable of supporting neurocritical care tasks that depend on synthesizing complex, time-varying data. Current evidence is strongest in neonatal encephalopathy and pediatric TBI, where models for EEG interpretation, seizure recognition assistance, multimodal physiologic fusion, MRI-based neuroprognostication, and outcome prediction have reached clinically meaningful performance in observational studies and early prospective evaluations. Neonatal applications often benefit from relatively standardized monitoring and imaging pathways (e.g., therapeutic hypothermia protocols), whereas older pediatric neurocritical cohorts are more heterogeneous in diagnoses, management, and endpoints, which can limit dataset size and transportability. Emerging pediatric work in stroke syndromes such as moyamoya, epilepsy beyond the neonatal period, and neuro-oncology highlights additional potential but remains largely heterogeneous and retrospective. The next phase should focus on rigorous multisite validation, calibrated and interpretable deployment, and prospective impact studies that demonstrate improved outcomes beyond improvements in AUROC alone.
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