Abstract
Cervical cancer stands out among various cancers due to its potential for prevention and eradication, mainly through vaccination and proactive screening measures. However, there are still a large number of women in low- and middle-income countries who need to undergo cervical cancer screening. Conventional cervical cancer screening approaches possess distinct benefits and drawbacks regarding sensitivity, specificity, promptness, and expense. In recent years, artificial intelligence (AI) has gained widespread use to help healthcare professionals in performing extensive cervical cancer screenings, focusing on machine learning (ML) and deep learning (DL) techniques. Traditional screening methods combined with AI technology have shown initial effectiveness in cervical cancer screening. But it is necessary to address various challenges such as limited technology and resources, difficulties in integrating clinical workflows, and ethical and legal risks in large-scale population cervical cancer screening. In this review, how AI helps simplify workflows, aids in cytological segmentation and diagnosis, enhances the triage and diagnosis processes for human papillomavirus (HPV) and colposcopy were described firstly. Then we summarized the existing clinical cases of AI applied to large-scale cervical cancer screening. Finally, we discussed the challenges and limitations of AI implementation in large population cervical cancer screening. These insights may possess the capacity to transform cervical cancer screening on a global scale by improving diagnostic precision, facilitating early intervention, and increasing the overall efficacy of cervical cancer screening initiatives worldwide.