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

AI in Neurocritical Care - Handouts

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This document provides a comprehensive overview of the evolving role of Artificial Intelligence (AI) in neurocritical care, emphasizing transformative impacts, challenges, and future directions. Dr. Masoom J. Desai highlights AI’s potential to shift neurocritical care from reactive to proactive management by integrating real-time multimodal data, enabling personalized medicine, enhanced clinical decision support systems (CDSS), and closed-loop systems. Research advancements focus on AI-driven phenotyping, improved patient selection for trials, and individualized protocols, while system transformations include reducing clinician burden, equitable AI design, and resource optimization.<br /><br />Key barriers to AI implementation are outlined across technical (data quality, model generalizability, explainability), clinical (workflow integration, AI literacy, trust), ethical (bias, accountability, privacy), research (interoperability, infrastructure), and sociocultural dimensions (clinician autonomy, patient perspective). Bias in AI models is a significant concern throughout development and deployment stages, necessitating mitigation strategies such as diverse standardized datasets, governance, AI education, and ongoing model maintenance emphasizing human-centric, ethical AI.<br /><br />Dr. Neeraj Badjatia’s contribution focuses on machine learning models for autonomic nervous system (ANS) dysfunction post-traumatic brain injury (TBI). Using high-frequency vital sign data, models like RAPiD-TBI predict early neurologic decline, leveraging continuous real-time monitoring with large data infrastructure supported by military and federal grants.<br /><br />Dr. Rohan Sharma discusses AI applications in subarachnoid hemorrhage (SAH), particularly AI-based volumetric analysis of noncontrast CT scans to quantify hemorrhage volume rapidly and accurately (SAHVAI model). This approach outperforms manual segmentation in speed and consistency, correlating hemorrhage volume with outcomes and complications such as vasospasm and delayed cerebral ischemia. AI volumetric assessment and predictive scoring systems like eSAH provide more objective, reproducible metrics to guide clinical decisions and research.<br /><br />Overall, the presentations advocate for responsible AI integration into neurocritical care through multidisciplinary collaboration, standardized data models, rigorous validation, clinician training, ethical governance, and equitable access, aiming to enhance clinical outcomes and augment physician capabilities rather than replacing clinicians. Future neurocritical care practice envisions interoperable data ecosystems, explainable and trustworthy AI, and human-centered systems transforming acute brain injury management over the coming decade.

Keywords

Artificial Intelligence

Neurocritical Care

Machine Learning

Traumatic Brain Injury

Subarachnoid Hemorrhage

Clinical Decision Support Systems

AI Ethics

Predictive Modeling

Multimodal Data Integration

Personalized Medicine

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