Responsible AI for Acute Stroke Management

Artificial intelligence is increasingly integrated into acute stroke management, demonstrating strong performance in tasks such as large vessel occlusion detection, Alberta Stroke Program Early Computed Tomography Score scoring, and functional outcome prediction. However, concerns persist regarding both the black-box nature of these models and algorithmic bias that may exacerbate disparities in stroke incidence, treatment, and outcomes across racial and socioeconomic subgroups. This review synthesizes the current literature on explainable artificial intelligence and fairness in artificial intelligence applications for acute stroke management, identifies persistent challenges, and outlines recommendations for the development of equitable and trustworthy systems in stroke care. Recent studies have increasingly adopted post hoc explainability methods, though these are limited by approximation and misleading interpretations, especially since explanations are rarely formally tested. Explainability and fairness remain largely disconnected, with fairness evaluation remaining uncommon due to limited demographic metadata, regulatory constraints, and the absence of stroke-specific fairness criteria. Generalizability also remains a concern due to suboptimal data set partitioning strategies and inadequate reporting practices. Responsible artificial intelligence for acute stroke management requires unified evaluation frameworks that jointly assess explainability, fairness, and generalizability.