Automation Bias in AI-Decision Support: Results from an Empirical Study
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.
Authors
- Ursula Hübner (ORCID: https://orcid.org/0000-0001-5372-2339)
- Niels Hannemann
- Jan-Oliver Kutza
- Joachim Dissemond (ORCID: https://orcid.org/0000-0003-3893-328X)
- Dorothee Busch (ORCID: https://orcid.org/0000-0001-5176-8157)
- Cornelia Erfurt‐Berge (ORCID: https://orcid.org/0000-0002-3722-6383)
- Mareike Przysucha (ORCID: https://orcid.org/0000-0001-8727-356X)
- Birgit Babitsch (ORCID: https://orcid.org/0000-0002-8722-3332)
- Maurice Moelleken (ORCID: https://orcid.org/0000-0002-5517-8696)
- Florian Kücking (ORCID: https://orcid.org/0009-0005-0808-6552)
Institutions
- Osnabrück University (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Universitätsklinikum Erlangen (DE)
- Essen University Hospital (DE)
- Hochschule Osnabrück (DE)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2024-08-30
- DOI
- https://doi.org/10.3233/shti240871
- Citations
- 19
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 8.18