An Explainable Agentic AI Framework for Uncertainty-Aware and Abstention-Enabled Acute Ischemic Stroke Imaging Decisions

📝 Original Info

• Title: An Explainable Agentic AI Framework for Uncertainty-Aware and Abstention-Enabled Acute Ischemic Stroke Imaging Decisions
• ArXiv ID: 2601.01008
• Date: 2026-01-03
• Authors: Md Rashadul Islam

📝 Abstract

Artificial intelligence (AI) models have demonstrated considerable potential in the imaging of acute ischemic stroke, especially in the detection and segmentation of lesions via computed tomography (CT) and magnetic resonance imaging (MRI). Nevertheless, the majority of existing approaches operate as black-box predictors, providing deterministic outputs without transparency regarding predictive uncertainty or the establishment of explicit protocols for decision rejection when predictions are ambiguous. This deficiency presents considerable safety and trust issues within the context of high-stakes emergency radiology, where inaccuracies in automated decision-making could conceivably lead to negative consequences in clinical settings. [1], [2]. In this paper, we introduce an explainable agentic AI framework targeted at uncertainty-aware and abstention-based decision-making in AIS imaging. It is based on a multistage agentic pipeline. In this framework, a perception agent performs lesion-aware image analysis, an uncertainty estimation agent estimates the predictive confidence at the slice level and a decision agent dynamically decides whether to make or withhold the prediction based on prescribed uncertainty threshold...

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