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. 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). 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; I² = 93%). 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. This study was supported by the 10.13039/501100001809 National Natural Science Foundation of China (No. 81901493) and the Shanghai Pujiang Program (No. 21PJD006).

Polycystic ovary syndrome (PCOS) is a common endocrine disorder with a complex pathogenesis that includes disordered follicle development, hypothalamic–pituitary–ovarian (HPO) axis dysfunction, hyperandrogenemia, and insulin resistance. The risk of complications during pregnancy, such as gestational diabetes mellitus (GDM) and preeclampsia (PE), among PCOS patients is higher than that in the general population. Kisspeptin (KP) is a peptide hormone widely expressed in the hypothalamus, limbic system, gonads, pancreas, and liver; it is highly expressed in the placenta and is considered to play an important role in pregnancy. Therefore, the aim of this review is to summarize the complex relationships among KP levels and pregnancy complications in PCOS and to provide a comprehensive understanding of the role of KP throughout pregnancy in PCOS patients. In our summary of the existing research, we provide information regarding the direct impact of high prepregnancy KP levels in PCOS patients on early embryo implantation and placental development, leading to abnormal KP levels during pregnancy and ultimately increasing the risk of complications such as gestational GDM and PE.

Endometriosis is associated with increased cardiovascular risk, but how its subtype heterogeneity informs this association remains unclear. This narrative review examines the biological characteristics of superficial peritoneal endometriosis (SUP), ovarian endometrioma (OMA), and deep infiltrating endometriosis (DIE) and their potential cardiovascular relevance. English-language literature identified through PubMed, Scopus, Google Scholar, and reference screening was synthesized across susceptibility factors, molecular and vascular alterations, and treatment exposures. Subtype-related findings highlight differences in anatomical location and tissue microenvironment, including peritoneal immune interactions in superficial lesions, intracystic iron accumulation and oxidative injury in OMA, and inflammatory and fibromuscular remodeling in deep lesions. These features provide a basis for investigating how lesion heterogeneity may relate to systemic lipid dysregulation, inflammation, endothelial dysfunction, and coagulation abnormalities. Ovarian function, surgical history, and hormonal regimens further inform interpretation of these relationships. By integrating phenotype and treatment exposure while distinguishing local findings from circulating biomarkers, vascular measurements, and clinical events, this review identifies evidence gaps linking biological heterogeneity to cardiovascular outcomes. Emerging subtype-related evidence supports further investigation but does not establish differences in cardiovascular event risk among SUP, OMA, and DIE. Prospective studies are needed to determine whether incorporating endometriosis subtype improves cardiovascular risk assessment beyond conventional factors. (Figure 1)