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Diagnostic accuracy of artificial intelligence-assisted radiology assessment of cancer: a systematic review

Dylan Zhao, Thomas Packer, Xiaobo Jie, Muhammad Shahid, Jason Oke, Annette Plüddemann · BJR|Artificial Intelligence · 2025

Abstract Objective Perform a systematic review and meta-analysis of studies using multi-reader multi-case (MRMC) study designs for cancer diagnosis with artificial intelligence (AI). Review diagnostic accuracy, study design and reporting. Methods A search of several databases between January 1, 2014 and February 28, 2024 was performed. Diagnostic accuracy studies that compared radiologists with and without AI-assistance in cancer diagnostic tasks over all imaging modalities were included. Meta-analysis using Summary Receiver Operating Characteristics (SROC) curves were plotted for pooled sensitivity and specificity. Risk of bias was assessed by using the Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C) and the Checklist for Artificial intelligence in Medical Imaging (CLAIM). Results Thirty-four studies were included of which 23 were included in meta-analysis. Eight identified cancers on Chest X-rays, 17 on CT, 9 on MRI. Pooled sensitivity and spec

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