Exploring the risks of automation bias in healthcare artificial intelligence applications: A Bowtie analysis

Abstract

This study conducts an in-depth review and Bowtie analysis of automation bias in AI-driven Clinical Decision Support Systems (CDSSs) within healthcare settings. Automation bias, the tendency of human operators to over-rely on automated systems, poses a critical challenge in implementing AI-driven technologies. To address this challenge, Bowtie analysis is employed to examine the causes and consequences of automation bias affected by over-reliance on AI-driven systems in healthcare. Furthermore, this study proposes preventive measures to address automation bias during the design phase of AI model development for CDSSs, along with effective mitigation strategies post-deployment. The findings highlight the imperative role of a systems approach, integrating technological advancements, regulatory frameworks, and collaborative endeavors between AI developers and healthcare practitioners to diminish automation bias in AI-driven CDSSs. We further identify future research directions, proposing quantitative evaluations of the mitigation and preventative measures.

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