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Artificial intelligence for clinical decision support in acute ischemic stroke: a systematic review

Background: - - Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support...

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Main Authors: Akay, Ela Marie (Author) , Hilbert, Adam (Author) , Carlisle, Benjamin G. (Author) , Madai, Vince I. (Author) , Mutke, Matthias Anthony (Author) , Frey, Dietmar (Author)
Format: Article (Journal)
Language:English
Published: June 2023
In: Stroke
Year: 2023, Volume: 54, Issue: 6, Pages: 1505-1516
ISSN:1524-4628
DOI:10.1161/STROKEAHA.122.041442
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1161/STROKEAHA.122.041442
Verlag, kostenfrei, Volltext: https://www.ahajournals.org/doi/10.1161/STROKEAHA.122.041442
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Author Notes:Ela Marie Z. Akay, Adam Hilbert, Benjamin G. Carlisle, Vince I. Madai, Matthias A. Mutke, Dietmar Frey
Description
Summary:Background: - - Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support to stroke clinicians. We review AI-based clinical decision support systems in the development stage, specifically regarding methodological robustness and constraints for clinical implementation. - - Methods: - - Our systematic review included full-text English language publications proposing a clinical decision support system using AI techniques for direct decision support in acute ischemic stroke cases in adult patients. We (1) describe data and outcomes used in those systems, (2) estimate the systems’ benefits compared with traditional stroke diagnosis and treatment, and (3) reported concordance with reporting standards for AI in healthcare. - - Results: - - One hundred twenty-one studies met our inclusion criteria. Sixty-five were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous. - - Conclusions: - - Our results suggest significant validity threats, dissonance in reporting practices, and challenges to clinical translation. We outline practical recommendations for the successful implementation of AI research in acute ischemic stroke treatment and diagnosis.
Item Description:Veröffentlicht: 22. Mai 2023
Gesehen am 09.08.2023
Physical Description:Online Resource
ISSN:1524-4628
DOI:10.1161/STROKEAHA.122.041442

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