Fig 1.
Paper’s organization.
Table 1.
Symbol interpretation table.
Fig 2.
Literature selection process.
Fig 3.
Results of the quality evaluation of the included literature.
Red represents high degree of bias, yellow represents unclear and green represents a low degree of preference.
Table 2.
Basic characteristics of the included studies.
Table 3.
Diagnostic features.
Fig 4.
OR forest plot of AI-assisted diagnostic system for lung cancer diagnosis.
Fig 5.
Forest plot of sensitivity and specificity of AI-aided diagnosis for lung cancer diagnosis.
Fig 6.
SROC curve for AI-assisted diagnosis of lung cancer.
Table 4.
Combined effect sizes for AI-assisted diagnostic systems for the diagnosis of lung cancer.
Table 5.
Results of STATA regression analysis for AI-assisted diagnosis of lung cancer.
Fig 7.
Results of the subgroup analysis of lung cancer diagnosed with the aid of artificial lung cancer diagnosis.
Fig 8.
Publication bias in AI-assisted diagnosis of lung cancer.