AI in Neurocritical Care: An Evolving Paradigm-AI in Neurocritical Care: An Evolving Paradigm

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Video Summary

The webcast "AI Neurocritical Care: An Evolving Paradigm" featured experts Niloufar Yalcin, Dr. Desai, Dr. Bajatia, and Dr. Sharma discussing the transformative potential, challenges, and future directions of AI in neurocritical care. Dr. Desai emphasized AI as a clinician-augmenting tool enabling real-time integration of diverse data to enable personalized, prescriptive care and improved outcomes. Key barriers include data quality, standardization, clinical trust, explainability, ethical/legal concerns, and integration into workflow, with bias arising from patient and practice variability being critical challenges. Mitigation strategies involve diverse multicenter data, governance, standardization, and maintaining human oversight. Dr. Bajatia highlighted AI models analyzing autonomic nervous system data to predict early traumatic brain injury (TBI) deterioration and guide lifesaving interventions. Their large institutional data registry captures extensive physiological data allowing development of real-time predictive tools and "digital twins" for forecasting treatment responses. Challenges include data capture, cleaning, computing power, and need for human-centered model visualization. Dr. Sharma presented AI-enabled volumetric analysis of subarachnoid hemorrhage (SAH), demonstrating development of automated, rapid, and accurate models predicting patient outcomes, improving upon traditional grading scales. AI advances in neuroimaging have already enhanced stroke care, and ongoing research aims to integrate AI tools into routine clinical decision support. The panel concluded that while promising, successful AI implementation demands multidisciplinary collaboration, education bridging medical and technical domains, robust data infrastructure, regulatory governance, and equitable design to ultimately transform neurocritical care.

Keywords

AI in neurocritical care

clinician-augmenting AI

real-time data integration

predictive models for TBI

digital twins in healthcare

AI volumetric analysis

subarachnoid hemorrhage prediction

multidisciplinary AI implementation

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