Stroke Prehospital Decision Support Systems Based on Artificial Intelligence: Grey Literature Scoping Review
Paper in proceeding, 2024

Stroke is a leading cause of mortality and disability worldwide. Therefore, there is a growing interest in prehospital point-of-care stroke clinical decision support systems (CDSSs), which with improved precision can identify stroke and decrease the time to optimal treatment, thereby improving clinical outcomes. Artificial intelligence (AI) may be a route to improve CDSSs for clinical benefit. Deploying AI in the area of prehospital stroke care is still in its infancy. There are several existing systematic and scoping reviews summarizing the progress of AI methods for stroke assessment. None of these reviews include grey literature, which could be a valuable source of information, especially when analysing future research and development directions. This paper aims to use grey literature to investigate stroke assessment CDSSs based on AI. The study adheres to PRISMA guidelines and presents seven records showcasing promising technologies. These records included three clinical trials, two smartphone applications, one master thesis and one PhD dissertation, which identify electroencephalogram (EEG), video analysis and voice and facial recognition as potential data sources for early stroke identification. The integration of these technologies may offer the prospect of faster and more accurate CDSSs in the future.

Stroke.

Clinical Decision Support Systems (CDSSs)

Grey Literature

Artificial Intelligence (AI)

Prehospital Care

Machine Learning(ML)

Author

Hoor Jalo

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Eunji Lee

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Mattias Seth

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Anna Bakidou

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Minna Pikkarainen

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Katarina Jood

University of Gothenburg

Bengt-Arne Sjöqvist

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Stefan Candefjord

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2

2184-4305 (ISSN)

Subject Categories

Medical Engineering

Neurology

Areas of Advance

Health Engineering

DOI

10.5220/0012380400003657

More information

Latest update

5/7/2024 6