Ethical Challenges and Solutions for Explainable AI in Clinical Decision Support Systems: From Black-Box Prediction to Trustworthy

Abstract

The rapid application of artificial intelligence (AI) in the healthcare sector has brought about unprecedented improvements in diagnostic accuracy; however, its inherent "black-box" nature creates an irreconcilable conflict with the fundamental requirement for transparency in clinical decision-making. This paper systematically reviews the ethical challenges and technical solutions associated with Explainable AI (XAI) within clinical decision support systems. It focuses specifically on analyzing how knowledge-enhanced hybrid models—by integrating biomedical knowledge graphs with lightweight neural networks—can generate natural-language explanations while simultaneously maintaining high predictive accuracy. Research indicates that knowledge graph-based multi-relational graph neural networks can effectively integrate heterogeneous, multi-source data—including genes, diseases, drugs, and phenotypes—demonstrating significant performance improvements over traditional methods in tasks involving isolated node prediction and rare disease inference (with AUC scores rising from 0.31 to 0.95). Furthermore, this paper proposes a four-dimensional ethical evaluation framework encompassing fairness, traceability, privacy protection, and the governance of algorithmic bias. A pilot study involving 1,200 patients across three hospitals in Italy demonstrated that this framework can effectively detect and mitigate algorithmic bias, successfully limiting the disparity in predictive accuracy among patients of different genders, ages, and socioeconomic backgrounds to within 2%. This paper argues that the deep integration of life sciences and comprehensive healthcare must be grounded in the cornerstones of explainability and fairness; this is not merely a technical issue, but a matter concerning the fundamental principles of medical ethics.