Explainable Artificial Intelligence (XAI) for Stroke Risk Prediction: Bridging Clinical Transparency and Machine Learning Precision

Abstract

Stroke remains a leading cause of death and long-term disability worldwide, and current risk stratification tools are often limited by coarse risk factors, population-specific calibration, and restricted capacity to incorporate high-dimensional clinical data. Data-driven machine learning models can substantially improve discriminative performance for stroke risk prediction, but their adoption at the point of care is hindered by the opacity of so-called “black-box” models and the associated medico-legal and ethical concerns. Explainable Artificial Intelligence (XAI) offers a principled set of techniques to interrogate complex models and generate human-interpretable rationales for individual and population-level predictions. This paper examines how XAI can be systematically integrated into stroke risk prediction pipelines to balance clinical transparency and machine learning precision. We outline an architecture that couples calibrated gradient-boosting and deep neural network models with model-agnostic and model-specific explanation methods, including Shapley value–based feature attribution, local surrogate models, and rule-based explanations. At the clinical interface, the framework emphasizes user-centred explanation design (e.g., risk-factor contribution plots, counterfactual scenarios, and pathway-oriented visualizations) aligned with neurologists’ and cardiologists’ decision workflows. We further discuss validation strategies that jointly assess discrimination, calibration, robustness to dataset shift, and the faithfulness and stability of explanations, and we highlight the role of reporting and governance frameworks for trustworthy medical AI. By synthesizing emerging empirical evidence and methodological advances, the paper argues that XAI-enabled stroke risk prediction can enhance clinician trust, support shared decision making, and provide a more auditable basis for deploying high-capacity models in routine care, while also clarifying open challenges around evaluation standards, human–AI interaction, and regulatory compliance.