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

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 te...

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Veröffentlicht in:Journal of Safety Science and Resilience = An quan ke xue yu ren xing (Ying wen) Jg. 5; H. 4; S. 460 - 469
Hauptverfasser: Abdelwanis, Moustafa, Alarafati, Hamdan Khalaf, Tammam, Maram Muhanad Saleh, Simsekler, Mecit Can Emre
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2024
KeAi Communications Co., Ltd
Schlagworte:
Artificial intelligence
Automation bias
Bowtie analysis
Decision support systems
Medical errors
Patient safety
Decision support systems
Patient safety
Bowtie analysis
Medical errors
Artificial intelligence
Automation bias
ISSN:2666-4496, 2666-4496
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Zusammenfassung: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.
ISSN:2666-4496
2666-4496
DOI:10.1016/j.jnlssr.2024.06.001