Do as AI say: susceptibility in deployment of clinical decision-aids
Name
s41746-021-00385-9.pdf
Description
Published version
Size
1.39 MB
Format
Adobe PDF
Checksum (MD5)
b915257109854583d1e52c3bb3b01a94
Author(s)
Gaube, Susanne
Suresh, Harini
Raue, Martina Julia
Merritt, Alexander
Berkowitz, Seth J.
Lermer, Eva
Coughlin, Joseph F
Guttag, John V.
Colak, Errol
Ghassemi, Marzyeh
Date Issued
February 2021
Journal
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Citation
Gaube, Susanne et al. "Do as AI say: susceptibility in deployment of clinical decision-aids." npj Digital Medicine 4, 1 (February 2021): doi.org/10.1038/s41746-021-00385-9. © 2021 The Author(s).
Version
Final published version
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 asked to evaluate advice quality and make diagnoses. All advice was generated by human experts, but some was labeled as coming from an AI system. As a group, radiologists rated advice as lower quality when it appeared to come from an AI system; physicians with less task-expertise did not. Diagnostic accuracy was significantly worse when participants received inaccurate advice, regardless of the purported source. This work raises important considerations for how advice, AI and non-AI, should be deployed in clinical environments.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Center for Transportation & Logistics
AgeLab (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41746-021-00385-9
