兰州大学学报（医学版）2026，Vol.52Issue(2)：53-59,7.DOI:10.13885/j.issn.2097-681X.T20250032
AI辅助CT在肺结节良、恶性诊断中的系统评价
Comparative performance of AI and radiologists in diagnosing pulmonary nodules on CT
摘要
Abstract
Objective To evaluate artificial intelligence(AI)assisted computed tomography(CT)in diagnos-ing benign and malignant pulmonary nodules.Methods Systematic searches were conducted through PubMed,The Cochrane Library,Embase,Web of Science,the China National Knowledge Infrastructure and China Biology Medicine disc for studies on AI,integrated CT evaluation of pulmonary nodules.By searching PubMed,The Cochrane Library,Embase,Web of Science,the China National Knowledge Infrastructure and China Biology Medicine disc to collect relevant studies on AI combined with CT evaluation of benign and ma-lignant pulmonary nodules.According to the established inclusion and exclusion criteria,the QUADAS-2 scale was used to evaluate the methodological quality of literature that met the requirements.RevMan5.3 and Stata18.0 were used for data integration and analysis,comparing pooled diagnostic metrics between AI sys-tems and radiologists,specificity,sensitivity,negative likelihood ratio,positive likelihood ratio,and diagnostic odds ratio.Inter-group differences were statistically tested using Z-tests.Summary receiver operating charac-teristic curves were constructed to visualize diagnostic accuracy,with heterogeneity explored through statis-tics.Subgroup analyses stratified by AI systems were conducted to compare the differences in sensitivity,spec-ificity,and other indicators between different systems.Results Pooled metrics demonstrated the following comparisons(AI-assisted CT vs.radiologist interpretation):sensitivity,specificity,positive likelihood ratio,negative likelihood ratio,diagnostic odds ratio,and summary receiver operating characteristiccurve area under the curve.Statistical comparisons revealed no significant inter-group differences(all P>0.05).Conclusion AI-assisted CT demonstrates diagnostic efficacy comparable to conventional radiologist interpretation in pulmo-nary nodule characterization.The marginally higher sensitivity suggests potential utility for reducing missed diagnoses,though large-scale clinical validation remains warranted.关键词
计算机断层扫描/人工智能/肺结节/人工智能辅助诊断/深度学习/Meta分析Key words
computed tomography/artificial intelligence/pulmonary nodule/artificial intelligence-assisted di-agnosis/deep learning/Meta-analysis分类
医药卫生引用本文复制引用
张蕾,杨嘉琪,辛小霞,符文杰,张常青,樊景春..AI辅助CT在肺结节良、恶性诊断中的系统评价[J].兰州大学学报（医学版）,2026,52(2):53-59,7.基金项目
甘肃省自然科学基金资助项目(23JRRA1292) （23JRRA1292）
