Abstract
Background: The field of cervical cancer care has been revolutionised by artificial intelligence (AI), enabling advanced analysis of imaging and multi-omics data, thereby enabling more accurate diagnosis, improved prediction of patient outcomes, and assistance with personalised treatments. Procedure: This systematic review is in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and utilises the PRISMA checklist. A comprehensive literature search was conducted using the databases of Web of Science, Scopus, IEEE and PubMed explore to identify the characteristics of studies published between 2021 and 2026. Publications were assessed for eligibility as long-form (original and review articles) relating to AI use for screening, diagnosis, prognosis, radiomics, pathology, and clinical decision support in relation to cervical cancer. All data synthesised were qualitative. Results: AI-based models enable accurate identification and classification of cervical neoplasia. They also facilitate earlier diagnosis through automated feature extraction from imaging. AI-assisted frameworks are beginning to support the development of precision medicine treatments tailored to individuals. Discussion: The clinical translation of AI into practice faces challenges such as data heterogeneity, insufficient external validation, and a lack of model interpretability. To address these, creating standardised data sets and developing interdisciplinary collaborations will enhance AI adoption in clinical use. Conclusion: There is substantial potential for AI to provide improved cervical cancer care. Future initiatives aimed at facilitating this include the creation of well-validated, standardized data sets and clinically interpretable models that ensure the implementation of AI into practice is successful in the real-world.