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

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

Blog Article

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 ORTHO-ADAPT 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 138 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.