Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: a systematic review and meta-analysis.

Background: Cervical cytology screening and colposcopy play crucial roles in cervical intraepithelial neoplasia (CIN) and cervical cancer prevention. Previous studies have provided evidence that artificial intelligence (AI) has remarkable diagnostic accuracy in these procedures. With this systematic review and meta-analysis, we aimed to examine the pooled accuracy, sensitivity, and specificity of AI-assisted cervical cytology screening and colposcopy for cervical intraepithelial neoplasia and cervical cancer screening. Methods: In this systematic review and meta-analysis, we searched the PubMed, Embase, and Cochrane Library databases for studies published between January 1, 1986 and August 31, 2024. Studies investigating the sensitivity and specificity of AI-assisted cervical cytology screening and colposcopy for histologically verified cervical intraepithelial neoplasia and cervical cancer and a minimum of five cases were included. The performance of AI and experienced colposcopists was assessed via the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) through random effect models. Additionally, subgroup analyses of multiple diagnostic performance metrics in developed and developing countries were conducted. This study was registered with PROSPERO (CRD42024534049). Findings: Seventy-seven studies met the eligibility criteria for inclusion in this study. The pooled diagnostic parameters of AI-assisted cervical cytology via Papanicolaou (Pap) smears were as follows: accuracy, 94% (95% CI 92-96); sensitivity, 95% (95% CI 91-98); specificity, 94% (95% CI 89-97); PPV, 88% (95% CI 78-96); and NPV, 95% (95% CI 89-99). The pooled accuracy, sensitivity, specificity, PPV, and NPV of AI-assisted cervical cytology via ThinPrep cytologic test (TCT) were 90% (95% CI 85-94), 97% (95% CI 95-99), 94% (95% CI 85-98), 84% (95% CI 64-98), and 96% (95% CI 94-98), respectively. Subgroup analysis revealed that, for AI-assisted cervical cytology diagnosis, certain performance indicators were superior in developed countries compared to developing countries. Compared with experienced colposcopists, AI demonstrated superior accuracy in colposcopic examinations (odds ratio (OR) 1.75; 95% CI 1.33-2.31; P < 0.0001; I Interpretation: These results underscore the potential and practical value of AI in preventing and enabling early diagnosis of cervical cancer. Further research should support the development of AI for cervical cancer screening, including in low- and middle-income countries with limited resources. Funding: This study was supported by the National Natural Science Foundation of China (No. 81901493) and the Shanghai Pujiang Program (No. 21PJD006).