Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges

Abstract

There is growing interest in exploiting the advances in artificial intelligence (AI) and machine learning (ML) for improving and \\monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concepts of interpretability and explainability are becoming increasingly essential to understand how ML algorithms could predict antimicrobial resistance or recommend specific therapeutic agents, in order to avoid unintended biases related to the “black box” nature of complex models. In this commentary, we review and discuss some hot topics on the use of ML algorithms for antimicrobial stewardship interventions, highlighting opportunities and challenges with peculiar attention to interpretability and explainability of employed models. As in other fields of medicine, the exponential growth of AI/ML shows the potential for improving the efficacy of antimicrobial stewardship interventions, at least in part by reducing time-consuming tasks for overwhelmed healthcare personnel. Improving our knowledge about how complex ML models work could further help to achieve crucial advances in promoting the appropriate use of antimicrobials, as well as in preventing antimicrobial resistance selection and dissemination.