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Objective: To systematically summarize and evaluate the current application status and research progress of artificial intelligence (AI) in cervical cancer screening in China. Methods: Literature related to the application of AI in cervical cancer screening in China was searched in PubMed, Embase, Cochrane Library, IEEE, China National Knowledge Infrastructure (CNKI), and Wanfang Database using the keywords"cervical cancer","artificial intelligence","screening","machine learning","deep learning","neural network","uterine cervical neoplasms,"uterine cervical tumor","diagnosis", and"China". The search was limited to studies published in Chinese and English. As of July 2025, a total of 35 eligible articles were included. Basic information from the included studies was extracted and summarized. In addition, the National Medical Products Administration (NMPA) official website was searched using the term"cervix"to identify approved AI-assisted cervical cancer screening products. Results: A total of 21 AI-assisted cervical cancer screening technologies were identified, including 17 technologies for primary screening, mainly AI-assisted cytology, and 4 technologies for colposcopic diagnosis. For AI-assisted cytology, the sensitivity ranged from 67.5% to 100.0% and the specificity ranged from 9.9% to 99.8% in hospital-based populations, with the overall accuracy of some technologies exceeding 90%. In community-based screening populations, the sensitivity ranged from 83.0% to 100.0% and the specificity ranged from 74.2% to 99.9%. Most studies suggested that AI could improve the diagnostic performance of pathologists to some extent, shorten the average slide-reading time, and enhance overall screening efficiency. A total of 24 AI-assisted cervical cancer screening products have been approved by the NMPA, all of which are AI-assisted cytology technologies, and corresponding studies were identified for 8 of these products. For AI-assisted colposcopic diagnosis used as a standalone screening modality, the sensitivity and specificity for identifying cervical intraepithelial neoplasia grade 2 (CIN2) or worse ranged from 43.6% to 95.5% and from 51.8% to 93.9%, respectively; for cervical intraepithelial neoplasia grade 3 (CIN3) or worse, the sensitivity ranged from 35.1% to 97.5% and the specificity ranged from 56.6% to 87.2%. In the physician-assisting mode, the sensitivity increased to 95.1%-97.5%, with improvements in interobserver consistency and diagnostic accuracy among less experienced colposcopists. Conclusions: AI has shown promising potential in cervical cancer screening in China. However, more scientific evidence is needed to determine whether it can be effectively integrated into the existing cervical cancer prevention and control system in China.