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Auditor models to suppress poor artificial intelligence predictions can improve human-artificial intelligence collaborative performance.

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Bibliographic Details
Title: Auditor models to suppress poor artificial intelligence predictions can improve human-artificial intelligence collaborative performance.
Authors: Brown KE; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Wrenn JO; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.; Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States., Jackson NJ; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Cauley MR; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Collins BX; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA., Novak LL; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States., Malin BA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.; Department of Computer Science, Vanderbilt University, Nashville, TN 37212, United States., Ancker JS; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Source: Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2026 Mar 01; Vol. 33 (3), pp. 621-631.
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1527-974X
DOI:10.1093/jamia/ocaf235