dc.contributor.author | Gaube, Susanne | |
dc.contributor.author | Suresh, Harini | |
dc.contributor.author | Raue, Martina Julia | |
dc.contributor.author | Merritt, Alexander | |
dc.contributor.author | Berkowitz, Seth J. | |
dc.contributor.author | Lermer, Eva | |
dc.contributor.author | Coughlin, Joseph F | |
dc.contributor.author | Guttag, John V. | |
dc.contributor.author | Colak, Errol | |
dc.contributor.author | Ghassemi, Marzyeh | |
dc.date.accessioned | 2021-04-12T19:22:25Z | |
dc.date.available | 2021-04-12T19:22:25Z | |
dc.date.issued | 2021-02 | |
dc.date.submitted | 2020-07 | |
dc.identifier.issn | 2398-6352 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/130457 | |
dc.description.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. | en_US |
dc.language.iso | en | |
dc.publisher | Springer Science and Business Media LLC | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1038/s41746-021-00385-9 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Nature | en_US |
dc.title | Do as AI say: susceptibility in deployment of clinical decision-aids | en_US |
dc.type | Article | en_US |
dc.identifier.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). | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Center for Transportation & Logistics | en_US |
dc.contributor.department | AgeLab (Massachusetts Institute of Technology) | en_US |
dc.relation.journal | npj Digital Medicine | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2021-04-06T15:51:17Z | |
dspace.orderedauthors | Gaube, S; Suresh, H; Raue, M; Merritt, A; Berkowitz, SJ; Lermer, E; Coughlin, JF; Guttag, JV; Colak, E; Ghassemi, M | en_US |
dspace.date.submission | 2021-04-06T15:51:18Z | |
mit.journal.volume | 4 | en_US |
mit.journal.issue | 1 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | |