Explainable Quantum Artificial Intelligence for Enhanced Radiology Decision Support

Abstract

The integration of Quantum Computing with Artificial Intelligence (AI) presents a transformative opportunity for advanced medical imaging analysis. This paper proposes an Explainable Quantum AI (XQAI) framework for radiology decision support systems, aimed at enhancing diagnostic accuracy while ensuring interpretability of results. The approach leverages quantum kernel-based feature extraction to capture complex spatial–spectral patterns in MRI, CT, and PET scans, enabling superior detection of subtle anomalies. A hybrid quantum- classical deep learning model is employed, where quantum circuits perform high-dimensional feature mapping, and classical neural networks execute classification tasks. To address the "black-box" challenge in AI-assisted diagnosis, the framework incorporates quantum explainability modules using measurement-driven saliency mapping and Shapley value–based feature attribution. This dual- layer explainability provides radiologists with both a quantum- informed feature importance ranking and a visual heatmap overlay on the medical image. Experimental evaluation on publicly available medical imaging datasets demonstrates improved detection sensitivity (7.3%) and diagnostic confidence compared to conventional deep learning approaches, while reducing model opacity. The proposed XQAI system offers a pathway towards trustworthy, high-performance, and clinically deployable quantum-assisted diagnostic tools in radiology.

Ähnliche Arbeiten