Diagnostic Performance of AI-Assisted Radiologists in Breast Cancer Detection Using Digital Mammography: A Systematic Review and Meta-Analysis.

IF 2.5 3区医学 Q2 ONCOLOGY

Clinical breast cancer Pub Date : 2025-08-21 DOI:10.1016/j.clbc.2025.08.013

Jiayin Lu, Xiaonan Xu, Yanyan Zhang, Kunyu Zhuang, Tali Fang, Chifa Zhang, Kairong Chen, Xiaomei Huang, Yingjia Li

{"title":"Diagnostic Performance of AI-Assisted Radiologists in Breast Cancer Detection Using Digital Mammography: A Systematic Review and Meta-Analysis.","authors":"Jiayin Lu, Xiaonan Xu, Yanyan Zhang, Kunyu Zhuang, Tali Fang, Chifa Zhang, Kairong Chen, Xiaomei Huang, Yingjia Li","doi":"10.1016/j.clbc.2025.08.013","DOIUrl":null,"url":null,"abstract":"<p><p>To evaluate the diagnostic performance of AI-assisted and standalone human radiologists in breast cancer detection using digital mammography (DM). A comprehensive search was conducted in PubMed, Web of Science, Embase, Cochrane Library, and Scopus databases for studies published from January 2019 to December 2024. Study quality was assessed using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) and quality assessment of diagnostic accuracy studies-comparative (QUADAS-C). Summary receiver operating characteristic (SROC) curves and prediction regions of pooled sensitivity, specificity, and estimated area under the curves (AUCs) were used to evaluate the diagnostic performance of AI-assisted radiologists versus standalone human radiologists. Sources of heterogeneity were explored using meta-regression analysis. Overall, 30 studies were included in the qualitative synthesis. Among them, data from 20 studies were separately utilized for quantitative synthesis, categorized into three scenario groups: concurrent assistant, AI reader-replacement, and additional reader scenarios. Pooled sensitivity was significantly higher for AI-assisted radiologists compared to standalone human radiologists in the concurrent scenario (0.84 vs. 0.78, P < .001), and pooled specificity was superior in the concurrent and replacement scenarios, respectively (0.84 vs. 0.80, P < .001; 0.96 vs. 0.95, P < .001). There were no significant differences in area under the curves (AUCs) among these three scenarios. In breast cancer diagnosis, AI-assisted radiologists demonstrated superior sensitivity compared to standalone human radiologists in the concurrent scenario, and superior specificity in both the concurrent and replacement scenarios. Further research is needed to confirm these findings and explore the optimal strategies for integrating AI into breast cancer diagnostic workflows.</p>","PeriodicalId":10197,"journal":{"name":"Clinical breast cancer","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical breast cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.clbc.2025.08.013","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

To evaluate the diagnostic performance of AI-assisted and standalone human radiologists in breast cancer detection using digital mammography (DM). A comprehensive search was conducted in PubMed, Web of Science, Embase, Cochrane Library, and Scopus databases for studies published from January 2019 to December 2024. Study quality was assessed using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) and quality assessment of diagnostic accuracy studies-comparative (QUADAS-C). Summary receiver operating characteristic (SROC) curves and prediction regions of pooled sensitivity, specificity, and estimated area under the curves (AUCs) were used to evaluate the diagnostic performance of AI-assisted radiologists versus standalone human radiologists. Sources of heterogeneity were explored using meta-regression analysis. Overall, 30 studies were included in the qualitative synthesis. Among them, data from 20 studies were separately utilized for quantitative synthesis, categorized into three scenario groups: concurrent assistant, AI reader-replacement, and additional reader scenarios. Pooled sensitivity was significantly higher for AI-assisted radiologists compared to standalone human radiologists in the concurrent scenario (0.84 vs. 0.78, P < .001), and pooled specificity was superior in the concurrent and replacement scenarios, respectively (0.84 vs. 0.80, P < .001; 0.96 vs. 0.95, P < .001). There were no significant differences in area under the curves (AUCs) among these three scenarios. In breast cancer diagnosis, AI-assisted radiologists demonstrated superior sensitivity compared to standalone human radiologists in the concurrent scenario, and superior specificity in both the concurrent and replacement scenarios. Further research is needed to confirm these findings and explore the optimal strategies for integrating AI into breast cancer diagnostic workflows.

查看原文本刊更多论文

人工智能辅助放射科医生在使用数字乳房x光检查乳腺癌诊断中的表现：系统回顾和荟萃分析。

评估人工智能辅助和独立的人类放射科医生在使用数字乳房x线摄影（DM）进行乳腺癌检测中的诊断性能。在PubMed、Web of Science、Embase、Cochrane Library和Scopus数据库中对2019年1月至2024年12月发表的研究进行了全面检索。采用诊断准确性研究质量评估2 （QUADAS-2）和诊断准确性研究质量评估-比较（QUADAS-C）对研究质量进行评估。摘要受试者工作特征（SROC）曲线和综合敏感性、特异性和曲线下估计面积（auc）的预测区域被用来评估人工智能辅助放射科医生与独立的人类放射科医生的诊断性能。采用元回归分析探讨异质性的来源。总的来说，30项研究被纳入定性综合。其中，分别利用20项研究的数据进行定量综合，将其分为并发助手、人工智能阅读器替代和附加阅读器三个场景组。人工智能辅助放射科医生在并发情况下的综合敏感性明显高于独立的人类放射科医生（0.84比0.78,P < .001），在并发和替代情况下的综合特异性分别优于人工智能辅助放射科医生（0.84比0.80,P < .001； 0.96比0.95,P < .001）。三种情况下的曲线下面积（auc）无显著差异。在乳腺癌诊断中，与独立的人类放射科医生相比，人工智能辅助放射科医生在并发情况下表现出更高的灵敏度，在并发和替代情况下都表现出更高的特异性。需要进一步的研究来证实这些发现，并探索将人工智能整合到乳腺癌诊断工作流程中的最佳策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Clinical breast cancer 医学-肿瘤学

CiteScore

5.40

自引率

3.20%

发文量

174

审稿时长

48 days

期刊介绍： Clinical Breast Cancer is a peer-reviewed bimonthly journal that publishes original articles describing various aspects of clinical and translational research of breast cancer. Clinical Breast Cancer is devoted to articles on detection, diagnosis, prevention, and treatment of breast cancer. The main emphasis is on recent scientific developments in all areas related to breast cancer. Specific areas of interest include clinical research reports from various therapeutic modalities, cancer genetics, drug sensitivity and resistance, novel imaging, tumor genomics, biomarkers, and chemoprevention strategies.