Artificial Intelligence in Cervical Cancer Screening: Current Evidence, Emerging Technologies, and Future Directions for Clinical Integration

Abstract

Cervical cancer remains one of the leading causes of cancer-related morbidity and mortality. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning (DL), have created new opportunities to enhance cervical cancer screening by improving diagnostic accuracy, reducing observer variability, and expanding access to screening services. This review critically examines the current applications of AI throughout the cervical cancer screening pathway, including cervical cytology, colposcopy, histopathology, human papillomavirus testing, and visual inspection with acetic acid (VIA). A comprehensive literature search was conducted using PubMed/MEDLINE, Embase, Cochrane Library, Web of Science, and Google Scholar for studies published between January 2015 and June 2026. Evidence from multiple screening modalities indicates that AI systems can achieve diagnostic performance comparable with, and in several settings superior to, experienced clinicians for identifying premalignant and malignant cervical lesions. Smartphone-based AI-assisted VIA has shown particular promise in resource-constrained settings by offering accurate, low-cost point-of-care screening. Recent studies further suggest that imaging device variability has a greater influence on model performance than geographic differences, while incremental retraining using relatively small local datasets can substantially improve model adaptability across different clinical environments. Early implementation studies from Indian primary healthcare settings have also demonstrated encouraging feasibility and diagnostic performance. AI has the potential to transform cervical cancer screening by improving diagnostic consistency, increasing screening coverage, and facilitating early detection, particularly in underserved populations.