Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke Care: A Systematic Review

Abstract Established randomized-trial-based parameters for acute ischemic stroke care fail to consider individualized patient data, leading to attempts to support or automate treatment and diagnosis decisions using artificial intelligence methods. We review existing research, specifically regarding methodological robustness, thereby identifying constraints for clinical AI implementation. Our systematic review of clinical decision support systems (CDSS) includes full-text English language publications proposing AI-based methods for decision support in acute ischemic stroke cases in adult patients. We (a) describe data and outcomes used in those systems, (b) estimate the systems’ benefits compared to traditional stroke diagnosis and treatment, and (c) report concordance with the MINIMAR checklist. 121 studies met our inclusion criteria. 65 were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous; adherence to the MINIMAR checklist was low. Our results suggest significant validity threats, dissonance in reporting practices and challenges to clinical translation. We outline practical recommendations for successful implementation of AI in acute ischemic stroke treatment and diagnosis.