The Role of XAI in Advice-Taking from a Clinical Decision Support System: A Comparative User Study of Feature Contribution-Based and Example-Based Explanations

Abstract

Explainable artificial intelligence (XAI) has shown benefits in clinical decision support systems (CDSSs); however, it is still unclear to CDSS developers how to select an XAI method to optimize the advice-taking of healthcare practitioners. We performed a user study on healthcare practitioners based on a machine learning-based CDSS for the prediction of gestational diabetes mellitus to explore and compare two XAI methods: explanation by feature contribution and explanation by example. Participants were asked to make estimates for both correctly and incorrectly predicted cases to determine if there were any over-reliance or self-reliance issues. We examined the weight of advice and healthcare practitioners’ preferences. Our results based on statistical tests showed no significant difference between the two XAI methods regarding the advice-taking. The CDSS explained by either method had a substantial impact on the decision-making of healthcare practitioners; however, both methods may lead to over-reliance issues. We identified the inclination towards CDSS use as a key factor in the advice-taking from an explainable CDSS among obstetricians. Additionally, we found that different types of healthcare practitioners had differing preferences for explanations; therefore, we suggest that CDSS developers should select XAI methods according to their target users.

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