# Explainable artificial intelligence

Using scientometric methods on 269 original research articles retained after PRISMA screening from Scopus and Web of Science, this study finds that the intellectual core of XAI research in decision dashboards is organized around explainable AI, decision making, and deep learning, that themes have shifted over time from classical machine learning such as neural networks and support vector machines toward visual deep learning, visualization techniques, trust, and intelligent decision support systems, and that the field is moving from a technology-centered toward a human- and decision-centered paradigm.

Using scientometric methods on 269 original research articles retained after PRISMA screening from Scopus and Web of Science, this study finds that the intellectual core of XAI research in decision dashboards is organized around explainable AI, decision making, and deep learning, that themes have shifted over time from classical machine learning such as neural networks and support vector machines toward visual deep learning, visualization techniques, trust, and intelligent decision support systems, and that the field is moving from a technology-centered toward a human- and decision-centered paradigm.

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