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Authors: Hoor Jalo 1 ; Eunji Lee 1 ; Mattias Seth 1 ; Anna Bakidou 1 ; 2 ; Minna Pikkarainen 1 ; 3 ; Katarina Jood 4 ; 5 ; Bengt Sjöqvist 1 and Stefan Candefjord 1

Affiliations: 1 Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden ; 2 PreHospen – Centre for Prehospital Research, University of Borås, Borås, Sweden ; 3 Department of Occupational Therapy, Oslo Metropolitan University, Oslo, Norway ; 4 Department of Clinical Neuroscience, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ; 5 Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden

Keyword(s): Artificial Intelligence (AI), Clinical Decision Support Systems (CDSSs), Grey Literature, Machine Learning (ML), Prehospital Care, Stroke.

Abstract: 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. (More)

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Paper citation in several formats:
Jalo, H.; Lee, E.; Seth, M.; Bakidou, A.; Pikkarainen, M.; Jood, K.; Sjöqvist, B. and Candefjord, S. (2024). Stroke Prehospital Decision Support Systems Based on Artificial Intelligence: Grey Literature Scoping Review. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 458-465. DOI: 10.5220/0012380400003657

@conference{healthinf24,
author={Hoor Jalo. and Eunji Lee. and Mattias Seth. and Anna Bakidou. and Minna Pikkarainen. and Katarina Jood. and Bengt Sjöqvist. and Stefan Candefjord.},
title={Stroke Prehospital Decision Support Systems Based on Artificial Intelligence: Grey Literature Scoping Review},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
year={2024},
pages={458-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012380400003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF
TI - Stroke Prehospital Decision Support Systems Based on Artificial Intelligence: Grey Literature Scoping Review
SN - 978-989-758-688-0
IS - 2184-4305
AU - Jalo, H.
AU - Lee, E.
AU - Seth, M.
AU - Bakidou, A.
AU - Pikkarainen, M.
AU - Jood, K.
AU - Sjöqvist, B.
AU - Candefjord, S.
PY - 2024
SP - 458
EP - 465
DO - 10.5220/0012380400003657
PB - SciTePress