{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:12:41Z","timestamp":1784117561191,"version":"3.55.0"},"reference-count":32,"publisher":"Korean Neurosurgical Society","issue":"3","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"content-domain":{"domain":["jkns.or.kr"],"crossmark-restriction":true},"short-container-title":["J Korean Neurosurg Soc"],"published-print":{"date-parts":[[2026,5,1]]},"abstract":"<jats:p>Neurocritical care relies on continuous assessment of neurological function and physiology under time pressure, yet bedside teams must interpret high-frequency, multimodal data that include hemodynamic waveforms, intracranial pressure (ICP), electroencephalography (EEG), near-infrared spectroscopy, neuroimaging, and electronic health record (EHR) context. Artificial intelligence (AI) and machine learning are increasingly used to fuse these biosignals, reduce interobserver variability, and generate dynamic risk estimates that can support monitoring, early warning, and prognostication. In pediatric and neonatal populations, where developmental physiology and smaller case volumes amplify uncertainty, AI-enabled tools have shown particularly strong performance in selected domains. Examples include prediction of impending intracranial hypertension using features from arterial blood pressure and ICP waveforms, automated EEG trend analysis for neurodevelopmental outcome prediction after perinatal asphyxia, magnetic resonance imagingbased neuroprognostication in neonatal hypoxic-ischemic encephalopathy, and interoperable EHR-based models for mortality and new morbidity risk stratification in the intensive care unit. However, many studies remain retrospective, and generalizability across institutions can be limited by differences in data capture, clinical practice, and outcome definitions. This review summarizes clinically relevant applications of multimodal biosignal analysis in neurocritical care, highlights validation and implementation considerations, and proposes priorities for future pediatric translation, including multisite evaluation, calibration, explainability, and prospective impact studies.<\/jats:p>","DOI":"10.3340\/jkns.2026.0046","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T23:17:13Z","timestamp":1774394233000},"page":"382-391","update-policy":"https:\/\/doi.org\/10.3340\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence in Neurocritical Care : Multimodal Biosignal Analysis for Prognosis, Monitoring, and Future Pediatric Applications"],"prefix":"10.3340","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7157-2119","authenticated-orcid":true,"given":"Taehoon","family":"Kim","sequence":"first","affiliation":[{"name":"Division of Pediatric Neurosurgery, 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