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

Digital Health Reviews (1724) Artificial Intelligence (2129) Health Services Research in Oncology (60) Computer-Aided Diagnosis (251) Imaging Informatics (302) Medical imaging (110) Gastroenterology and Hepatology (127)

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
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Journals

  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