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

Area of Science:

• Neuroscience
• Medical Informatics

Background:

• Continuous EEG (cEEG) monitoring is expanding in critical care.
• A gap exists between cEEG data acquisition and clinical interpretation capacity.
• Advances in EEG automation offer potential for efficient interpretation.

Purpose of the Study:

• Review the historical development of EEG analysis from automation to AI.
• Examine the application of these tools in neurocritical care.
• Discuss benefits, limitations, and practical implementation of automated and AI-assisted EEG.

Main Methods:

• Historical review of EEG analysis methods.
• Examination of quantitative EEG and AI in EEG processing.
• Analysis of AI's role in clinical decision-making integrated with EMR systems.

Main Results:

• AI and automation show promise for efficient EEG interpretation.
• Limited evidence currently guides the safe and ethical use of these tools.
• AI can support clinical decision-making when integrated with EMRs.

Conclusions:

• Automated and AI-assisted tools can augment, not replace, clinical expertise.
• These tools can improve the timeliness and quality of patient-centered care in neurocritical settings.
• Focus on practical implementation and real-world constraints is crucial for successful integration.