Advanced AI-based technologies for early detection and personalized management of cervical cancer

Abstract

Cervical cancer is one of the leading causes of cancer-related deaths among women worldwide, imposing a particularly heavy burden in low- and middle-income countries. In recent years, advanced artificial intelligence (AI)-based technologies, including convolutional neural networks (CNNs) for image analysis and natural language processing (NLP) of electronic health records (EHRs), have substantially improved detection performance, individualized risk prediction, and the design of tailored treatment regimens. By leveraging expert visual recognition and synthesizing multimodal clinical data, these approaches offer the potential for more accurate screening and faster diagnosis. However, routine adoption hinges on resolving issues of data heterogeneity, algorithm interpretability, and ethical deployment. In this review, we summarize the latest AI breakthroughs in cervical cancer management, emphasize their promise for enhancing early intervention and personalized therapy, and call for rigorous validation to ensure safe, equitable integration into practice.

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