Systematic literature review on the application of explainable artificial intelligence in palliative care studies

Abstract

Given the critical role of AI-driven decisions in patient care, adopting XAI techniques is essential for fostering trust and usability. Although progress has been made, significant gaps persist. A main challenge remains the trade-off between model performance and interpretability, as highly accurate models often lack the transparency required to build trust in clinical settings. Additionally, complex models frequently provide inadequate explanations for their outputs, lack consistent documentation, and have limited XAI applications, reducing the interpretability of machine learning studies for clinicians and decision-makers.

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