Advances in artificial intelligence for the early detection of cervical cancer in adult women: a scoping review

Abstract Objective: To synthesize current scientific evidence on the benefits and potential contributions of integrating AI-based technologies with traditional diagnostic methods used for the detection and early diagnosis of cervical cancer in adult women. Methods: An exhaustive search was conducted in academic databases (PubMed, Scopus and BIREME) using specific search terms and Boolean operators in December 2024. Independently conducted by three researchers across all databases, the selection process included articles involving adult women with either suspected or confirmed cervical cancer, in which the application of artificial intelligence (AI) technologies was examined across various techniques used for early diagnosis of the disease, such as cytology, colposcopy, and radiological imaging, among others. Only articles published between 1999 and 2022 were included. The included articles were reviewed in full text by all authors, and relevant data were extracted and organized into a chart comprising the following items: author and year of publication, title, study design, type of AI technology used, and a summary of the content. Results: AI, particularly through Machine Learning (ML) algorithms, demonstrated significant improvements in the accuracy, sensitivity, and efficiency of cervical lesion classification when combined with conventional diagnostic techniques like cervical cytology, colposcopy, and biopsy. This combined approach outperformed traditional methods used in isolation. Conclusion: The integration of AI with standard cervical cancer screening and diagnostic methods offers substantial benefits, including faster detection times, reduced workload for pathologists, and improved patient outcomes by facilitating earlier treatment initiation and reducing diagnostic variability. Considering the available literature, the use of AI may offer potential benefits; however, further studies are required.