Applications of Bioinformatics in Cervical Cancer Detection and Management With The Help of Artificial Intelligence

Abstract

Cervical cancer remains a major global health concern, particularly in low- and middle-income countries, despite the availability of screening programs and prophylactic human papillomavirus (HPV) vaccination. Conventional diagnostic methods such as Pap smear cytology and HPV DNA testing are limited by subjective interpretation, variable sensitivity in early-stage lesions, and insufficient capability for personalized disease management. In this context, the integration of bioinformatics and artificial intelligence (AI) has emerged as a powerful paradigm for improving cervical cancer detection, prognosis, and clinical decision-making through the analysis of large-scale biological, imaging, and clinical datasets.

This review provides a comprehensive overview of recent advances in AI-driven bioinformatics applications for cervical cancer management, including genomic and transcriptomic analyses for biomarker discovery, proteomic and metabolomic profiling for early diagnosis, deep learning–based interpretation of cytology and histopathology images, and integrative multi-omics approaches for predicting disease progression and therapeutic response. Although these technologies have demonstrated promising diagnostic accuracy and predictive performance in research settings, their translation into routine clinical practice remains limited.

A critical limitation in AI-driven bioinformatics for cervical cancer is the lack of large, standardized, and ethnically diverse multi-modal datasets, which restricts model training, validation, and reliable application across diverse populations.