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Although machine learning (ML) models of AI achieve high performances inmedicine, they are not free of errors. Empowering clinicians to identifyincorrect model recommendations is crucial for engendering trust in medical AI.Explainable AI (XAI) aims to address this requirement by clarifying AIreasoning to support the end users. Several studies on biomedical imagingachieved promising results recently. Nevertheless, solutions for models usingtabular data are not sufficient to meet the requirements of clinicians yet.This paper proposes a methodology to support clinicians in identifying failuresof ML models trained with tabular data. We built our methodology on three mainpillars: decomposing the feature set by leveraging clinical context latentspace, assessing the clinical association of global explanations, and LatentSpace Similarity (LSS) based local explanations. We demonstrated ourmethodology on ML-based recognition of preterm infant morbidities caused byinfection. The risk of mortality, lifelong disability, and antibioticresistance due to model failures was an open research question in this domain.We achieved to identify misclassification cases of two models with ourapproach. By contextualizing local explanations, our solution providesclinicians with actionable insights to support their autonomy for informedfinal decisions.

Context-dependent Explainability and Contestability for Trustworthy  Medical Artificial Intelligence: Misclassification Identification of  Morbidity Recognition Models in Preterm Infants