Detection of high-grade squamous intraepithelial lesions (HSILs) is key for the prevention of human papillomavirus (HPV)–related cancers. In this study

we aimed to identify and consolidate the existing evidence on the diagnostic performances of artificial intelligence (AI)–assisted tools for HPV-related HSILs. We followed the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy and the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy guidelines and systematically searched PubMed

Embase

Web of Science

IEEE

and the Cochrane Library for eligible studies published between 2000 and January 2024. Studies on AI-assisted colposcopy used to facilitate the diagnosis of cervical or anal HSILs and cancers were included. The revised Quality Assessment of Diagnostic Studies 2 and AI checklists were used to assess t

he risk of bias. With random-effect models

meta-analyses were performed on performance indicators

including accuracy

sensitivity

specificity

and area under the receiver-operating characteristic curves (AUC). Subgroup and meta-regression analyses were performed to identify the sources of heterogeneity. Twenty-five studies

comprising 21 studies focused on cervical cancer and 4 on anal cancer

were included in the meta-analysis. When differentiating from individuals with cervical intraepithelial neoplasia grade 2 (CIN2)/low-grade squamous intraepithelial lesion (LSIL)− (lesion grade CIN2 or ≤LSIL)

the AI-assisted diagnostic tools identified individuals with CIN2+/HSIL+ (lesion grade ≥CIN2 or ≥HSIL) at a pooled accuracy of 0.84 (95% confidence interval [CI

]

: 0.81–0.88)

sensitivity of 0.87 (0.81–0.93)

specificity of 0.85 (0.81–0.89)

and AUC of 0.94 (0.91–0.96). For anal cancer

the AI-assisted diagnostic tools differentiated individuals with anal intraepithelial neoplasia grade 2 (AIN2)+/HSIL+ (≥AIN2 or ≥HSIL) from those with AIN2/LSIL− (lesion grade AIN2 or ≤LSIL) at a pooled accuracy of 0.90 (95% CI: 0.88–0.93)

sensitivity of 0.94 (0.87–0.99)

specificity of 0.88 (0.71–0.98)

and AUC of 0.97 (0.95–0.98). All meta-analysis results demonstrated high heterogeneity (

I

2

90%)

but the meta-regression analysis did not reveal any significant contributing sources of heterogeneity. Based on this systematic review and meta-analysis of existing literature

we concluded that AI-assisted diagnostic tools have demonstrated promising predictive performance in diagnosing HPV-related cervical and anal precancerous lesions and cancers.