Automating Critical Care EEG: Enhancing Accuracy, Efficiency, and Patient Outcomes in Critical Care

The rapid expansion of continuous EEG (cEEG) monitoring in critically ill patients has created a growing gap between data acquisition and the clinical capacity to interpret these data in a timely manner. This mismatch has emerged alongside major advances in EEG automation, which promise to support more efficient and clinically integrated interpretation. At the same time, evidence guiding the optimal, safe, and ethically responsible use of these tools remains limited. In this review, we trace the historical development of EEG analysis methods from early automation to contemporary AI-based approaches and examine their application in neurocritical care. We discuss the potential benefits and limitations of quantitative EEG and AI for EEG processing, as well as the role of AI in supporting clinical decision-making in these populations when integrated with electronic medical record systems. By focusing on practical implementation and real-world constraints, we aim to show how automated and AI-assisted tools can augment, rather than replace, clinical expertise and help improve the timeliness and quality of patient-centered care.