Do as AI say: susceptibility in deployment of clinical decision-aids
Abstract Artificial intelligence (AI) models for decision support have been developed for clinical settings such as radiology, but little work evaluates the potential impact of such systems. In this study, physicians received chest X-rays and diagnostic advice, some of which was inaccurate, and were...
Main Authors: | Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J. Berkowitz, Eva Lermer, Joseph F. Coughlin, John V. Guttag, Errol Colak, Marzyeh Ghassemi |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-02-01
|
Series: | npj Digital Medicine |
Online Access: | https://doi.org/10.1038/s41746-021-00385-9 |
Similar Items
-
Do as AI say: susceptibility in deployment of clinical decision-aids
by: Gaube, Susanne, et al.
Published: (2021) -
Risks Seem Low While Climbing High: Shift in Risk Perception and Error Rates in the Course of Indoor Climbing Activities
by: Martina Raue, et al.
Published: (2018-12-01) -
Hand(y) hygiene insights: Applying three theoretical models to investigate hospital patients' and visitors' hand hygiene behavior.
by: Susanne Gaube, et al.
Published: (2021-01-01) -
How a smiley protects health: A pilot intervention to improve hand hygiene in hospitals by activating injunctive norms through emoticons.
by: Susanne Gaube, et al.
Published: (2018-01-01) -
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
by: Suresh, Harini, et al.
Published: (2022)