Public articles linked to the same research event.
Stroke 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
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
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
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