Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews

作者：Haishan Xu, Ting‐Ting Gong, Xin‐Jian Song, Qian Chen, Qi Bao, Wei Yao, Meng-Meng Xie, Chen Li, Marcin Grzegorzek, Yu Shi, Hongzan Sun, Xiaohan Li, Yuhong Zhao, Song Gao, Qi‐Jun Wu · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/53567 · 被引用次数：14 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment

BACKGROUND: Artificial intelligence (AI) has the potential to transform cancer diagnosis, ultimately leading to better patient outcomes. OBJECTIVE: We performed an umbrella review to summarize and critically evaluate the evidence for the AI-based imaging diagnosis of cancers. METHODS: PubMed, Embase, Web of Science, Cochrane, and IEEE databases were searched for relevant systematic reviews from inception to June 19, 2024. Two independent investigators abstracted data and assessed the quality of evidence, using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. We further assessed the quality of evidence in each meta-analysis by applying the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria. Diagnostic performance data were synthesized narratively. RESULTS: In a comprehensive analysis of 158 included studies evaluating the performance of AI algorithms in noninvasive imaging diagnosis across 8 major human system cancers, the accuracy of the classifiers for central nervous system cancers varied widely (ranging from 48% to 100%). Similarities were observed in the diagnostic performance for cancers of the head and neck, respiratory system, digestive system, urinary system, female-related systems, skin, and other sites. Most meta-analyses demonstrated positive summary performance. For instance, 9 reviews meta-analyzed sensitivity and specificity for esophageal cancer, showing ranges of 90%...