Augmenting Decision Competence in Healthcare Using AI-based Cognitive Models

Abstract

In many critical decisions, such as medicine, transparency of the underlying decision process is critical. This extends to decision processes that are supported by artificial intelligence. Rather than using a post-hoc explainability approach from explainable AI research (SHAP or LIME), we develop and test an intrinsically transparent and intuitively interpretable model developed from cognitive science, fast-and-frugal trees, in a comparative analysis with state-of-the-art machine learning models. The resultant decision support can be easily implemented as laminated pocket card, augmenting the decision competence of physicians rather than replacing it.

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