Responsible AI for Acute Stroke Management: A Review of Explainability and Fairness
Synopsis
This review synthesizes the current literature on explainable and fair artificial intelligence in acute stroke management, noting that AI already performs strongly on tasks such as large vessel occlusion detection, Alberta Stroke Program Early Computed Tomography Score scoring, and functional outcome prediction, while post hoc explainability methods remain approximate and rarely formally tested, fairness evaluation remains uncommon due to limited demographic metadata, regulatory constraints, and the absence of stroke-specific fairness criteria, explainability and fairness remain largely disconnected, and generalizability is affected by dataset partitioning and reporting practices, leading to a call for unified evaluation frameworks that jointly assess explainability, fairness, and generali
Interpretation
The review confirms that AI has demonstrated strong performance in key acute stroke management tasks, including large vessel occlusion detection, Alberta Stroke Program Early Computed Tomography Score scoring, and functional outcome prediction. It consolidates progress scattered across different tasks into an overall picture of AI in acute stroke care. Based on a synthesis of existing literature, this is review-level evidence; the text provides no specific sample sizes or effect sizes.
Recent studies have increasingly adopted post hoc explainability methods, but these are limited by approximation and misleading interpretations, and explanations are rarely formally tested. It reframes explainability from a purely technical option into an evaluation problem that requires validation. Derived from an induction over current literature trends, with the text describing limitations as 'limited by approximation and misleading interpretations' and 'rarely formally tested'.
Explainability and fairness remain largely disconnected, and fairness evaluation is uncommon due to limited demographic metadata, regulatory constraints, and the absence of stroke-specific fairness criteria. It shows that barriers to fairness evaluation are not only technical but also involve data, regulation, and standard-setting. A review-level judgment; the text explicitly lists three categories of barriers without quantitative synthesis.
Generalizability remains a concern due to suboptimal dataset partitioning strategies and inadequate reporting practices; responsible AI for acute stroke management requires unified frameworks that jointly assess explainability, fairness, and generalizability. It moves the three evaluation dimensions from separate tracks toward joint assessment as a direction for future system development. A recommendation-based conclusion from a literature review, representing a directional claim rather than an empirically tested result.
Perspective
The conclusions are framed for acute stroke management and are relevant to researchers, developers, and regulatory stakeholders focused on tasks such as large vessel occlusion detection, Alberta Stroke Program Early Computed Tomography Score scoring, and functional outcome prediction; the proposed unified evaluation framework is intended to guide joint assessment of explainability, fairness, and generalizability rather than to provide a deployable model or clinical protocol.
Readers should still watch how post hoc explainability methods are formally tested in clinical decision-making; how fairness evaluation can be implemented under limited demographic metadata and regulatory constraints; which dimensions stroke-specific fairness criteria should include; and how unified evaluation frameworks perform across different datasets and health systems. Because the current text is a review-level summary without figures or detailed study information, the extent to which these questions are developed in the original remains to be examined.