Automation Bias in AI-Decision Support: Results from an Empirical Study.

Autor:	Kücking F; Health Informatics Research Group, Osnabrück University of Applied Science, Osnabrück, Germany., Hübner U; Health Informatics Research Group, Osnabrück University of Applied Science, Osnabrück, Germany., Przysucha M; Health Informatics Research Group, Osnabrück University of Applied Science, Osnabrück, Germany., Hannemann N; Department of New Public Health, Osnabrück University, Osnabrück, Germany., Kutza JO; Health Informatics Research Group, Osnabrück University of Applied Science, Osnabrück, Germany.; Department of New Public Health, Osnabrück University, Osnabrück, Germany., Moelleken M; Department of Dermatology, Venerology and Allergology, University Hospital of Essen, Essen, Germany., Erfurt-Berge C; Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlgange, Germany., Dissemond J; Department of Dermatology, Venerology and Allergology, University Hospital of Essen, Essen, Germany., Babitsch B; Department of New Public Health, Osnabrück University, Osnabrück, Germany., Busch D; Health Informatics Research Group, Osnabrück University of Applied Science, Osnabrück, Germany.; Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlgange, Germany.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 30; Vol. 317, pp. 298-304.
DOI:	10.3233/SHTI240871
Abstrakt:	Introduction: Automation bias poses a significant challenge to the effectiveness of Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy. Previous research highlights trust, self-confidence, and task difficulty as key determinants. With the increasing availability of AI-enabled CDSS, automation bias attains new attention. This study therefore aims to identify factors influencing automation bias in a diagnostic task. Methods: A quantitative intervention study with participants from different backgrounds (n = 210) was conducted, employing regression analysis to analyze potential factors. Automation bias was measured as the agreement rate with wrong AI-enabled recommendations. Results and Discussion: Diagnostic performance, certified wound care training, physician profession, and female gender significantly reduced false agreement rates. Higher perceived benefit of the system was significantly associated with promoting false agreement. Strategies like comprehensive diagnostic training are pivotal in the prevention of automation bias when implementing CDSS. Conclusion: Considering factors influencing automation bias when introducing a CDSS is critical to fully leverage the benefits of such a system. This study highlights that non-specialists, who stand to gain the most from CDSS, are also the most susceptible to automation bias, emphasizing the need for specialized training to mitigate this risk and ensure diagnostic accuracy and patient safety.
Databáze:	MEDLINE
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