AI辅助CT在肺结节良、恶性诊断中的系统评价OA

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.