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This paper is in the following e-collection/theme issue:

Digital Health Reviews (1884) Clinical Information and Decision Making (2167) Obstetrics, Gynecology, and Reproductive Medicine (353) Innovations and Technology for Cancer Prevention and Screening (220) Imaging Informatics (358) Artificial Intelligence (2482) Innovations in Cancer Diagnostic and Decision Support (125) Computer-Aided Diagnosis (290) Theme Issue: Reproductive Informatics (64)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66530, first published .
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Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis

Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis

Authors of this article:

Longyun Wang1 Author Orcid Image ;   Zeyu Wang1 Author Orcid Image ;   Bowei Zhao1 Author Orcid Image ;   Kai Wang1 Author Orcid Image ;   Jingying Zheng2 Author Orcid Image ;   Lijing Zhao1 Author Orcid Image
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Journals

  1. Li R, Zou K, Ma Q, Liu Y, Wang X, Huang W, Gao S, Yang X. Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology. Frontiers in Oncology 2026;15 View
  2. GONG Y, CUI Y, MA C, MIN S, WANG P, MA L. DEEP LEARNING-BASED MAGNETIC RESONANCE IMAGING ARTIFICIAL INTELLIGENCE CLASSIFICATION TECHNOLOGY IN THE DIAGNOSIS OF ENDOMETRIAL CANCER. Journal of Mechanics in Medicine and Biology 2026 View

Conference Proceedings

  1. Shukla G, Awasthi V, Nipane S, Upadhyay D, Panthakkan A, Appathil M, Balusamy B, Al-Ahmad H. 2025 8th International Conference on Signal Processing and Information Security (ICSPIS). Deciphering Molecular Subtypes and Histological Patterns in Uterine Corpus Endometrial Carcinoma View