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

Digital Health Reviews (1869) Imaging Informatics (350) Gastroenterology and Hepatology (148) Artificial Intelligence (2444) Health Services Research in Oncology (73) Computer-Aided Diagnosis (285) Medical imaging (143)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78306, first published .
Diagnostic Performance of Computed Tomography–Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis

Diagnostic Performance of Computed Tomography–Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis

Diagnostic Performance of Computed Tomography–Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis

Authors of this article:

Jie Chen1 Author Orcid Image ;   Jianxin Xi2 Author Orcid Image ;   Tianyu Chen3 Author Orcid Image ;   Lulu Yang1 Author Orcid Image ;   Kaijia Liu1 Author Orcid Image ;   Xiaobo Ding1 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

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  1. Su P, Shih H, Xu J. Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study. JMIR Medical Informatics 2025;13:e80351 View
  2. Cong F, Tian K, Gao Q, Wang F, Sun P, Xu N. CT Radiomics–Based Machine Learning Model for Predicting Capsular and Neural Invasion in Thyroid Carcinoma: Diagnostic Accuracy Study. JMIR Medical Informatics 2026;14:e77349 View
  3. Mo C, Hu X, Yuan Z, Liu T. Advances in In Vitro Diagnostics for Cholangiocarcinoma: From Biomarker Discovery to Artificial Intelligence. International Journal of Molecular Sciences 2026;27(9):3779 View