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