Knowledge Graphs for Cervical Cancer Across the Care Continuum: From Vaccination and Screening to Personalized Therapy.

Cervical cancer remains one of the leading causes of cancer-related death among women worldwide, with a disproportionately high burden in low- and middle-income countries. Infection with human papillomavirus (HPV) is the central etiological factor. Although HPV vaccination and standardized screening can substantially reduce incidence and mortality, major bottlenecks persist in real-world settings, including inadequate coverage, loss to follow-up, data silos, and limited interpretability of algorithms. Knowledge graphs (KGs), characterized by their core "entity-relation" structure, can integrate electronic medical records, HPV genotyping, pathology/imaging, multi-omics, and public health data within a unified semantic framework, and provide traceable evidence chains and reasoning pathways to support clinical decision-making. KGs may become an important enabler. This review summarizes the potential mechanisms and application paradigms of KGs along the full continuum of cervical cancer prevention and care, including primary prevention (vaccination strategies and health education), screening and diagnosis (multimodal data integration, explainable AI, and clinical decision support systems, CDSS), as well as therapeutic decision-making and follow-up management (molecular subtyping, drug associations, and recurrence monitoring. We further highlight key challenges such as ontology standardization, cross-institutional interoperability, dynamic knowledge updating, and reliability of automated knowledge extraction. Future work should focus on establishing reusable, engineering-oriented pathways for the synergy of "KG + large language models (LLMs)/multimodal AI", the development of explainability evaluation metrics, privacy-compliant frameworks, and deployable solutions for primary care settings, in order to accelerate progress toward the global cervical cancer elimination goal.