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Leveraging artificial intelligence to decipher gynecologic cytopathology reports: Insights from an exploratory study for possible use in patient portals.

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Bibliographic Details
Title: Leveraging artificial intelligence to decipher gynecologic cytopathology reports: Insights from an exploratory study for possible use in patient portals.
Authors: Lichtenberg RS; Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois, USA., Mon KS; Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois, USA., Mehrotra S; Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois, USA., Trabzonlu L; Department of Pathology, University of Illinois Medical Center, Chicago, Illinois, USA., Aragao A; Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois, USA., Gordezky R; Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois, USA., Akdas Y; Community Health and Engagement, Vanderbilt University Medical Center, Nashville, Tennessee, USA., Barkan GA; Department of Pathology and Laboratory Medicine, Loyola University Medical Center, Maywood, Illinois, USA.
Source: Cancer cytopathology [Cancer Cytopathol] 2026 Mar; Vol. 134 (3), pp. e70077.
Publication Type: Journal Article
Journal Info: Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 101499453 Publication Model: Print Cited Medium: Internet ISSN: 1934-6638 (Electronic) Linking ISSN: 1934662X NLM ISO Abbreviation: Cancer Cytopathol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1934-6638
DOI:10.1002/cncy.70077