Artificial Intelligence (AI) is an important product of the rapid development of computer technology today. It has a far-reaching impact on the development of medical diagnostic technology especially in combination with medical imaging. The aim of this study was to analyze the diagnostic accuracy of AI-assisted diagnosis technology for classification of benign and malignant lung nodules on Computerized Tomography (CT) images.

A meta-analysis was conducted of published research articles on diagnostic accuracy of AI-assisted diagnosis technology for lung nodules classification between 2010 and 2019 in the databases of PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform and China Bio-medicine Database. Statistical analysis was performed with the software SAS 9.4 and Stata 12.0, and the summary receiver operating characteristic (SROC) curve was drawn to evaluate accuracy of the method.

A total of 27 studies with 5,701 lung nodules were considered. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and the area under the curve of SROC for AI-assisted diagnosis technology for lung nodules classification respectively were 0.892 (95% confidence interval [CI]: 0.854–0.920), 0.876 (95% CI: 0.833–0.909), 7.190 (95% CI: 5.194–9.955), 0.124 (95% CI: 0.089–0.171), 58.102 (95% CI: 32.391–104.219) and 0.95 (95%CI: 0.92–0.96).

Of note, several limitations should be considered when interpreting the findings of this meta-analysis. Data acquisition is not comprehensive enough because the language of the literature search was limited to Chinese and English. Furthermore,heterogeneity caused due to the difference of lung nodule size affected the study results. Despite these limitations, our study suggests that AI-assisted diagnosis technology for benign-malignant lung nodule classification on CT images obtains high diagnostic accuracy, and it can be used as a novel method to differentiate benign and malignant pulmonary nodules.