Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming cervical cancer research and offering advancements in diagnosis, prognosis, screening, and treatment. This review explores ML applications with particular emphasis on prediction models. A comprehensive literature search identified studies using ML for survival prediction, risk assessment, and treatment optimization. ML-driven prognostic models integrate clinical, histopathological, and genomic data to improve survival prediction and patient stratification. Screening methods, including deep-learning-based cytology analysis and human papillomavirus detection, enhance accuracy and efficiency. ML-driven imaging techniques facilitate early and precise cancer diagnosis, while risk prediction models assess susceptibility based on demographic and genetic factors. AI also optimizes treatment planning by predicting therapeutic responses and guiding personalized interventions. Despite significant progress, challenges remain regarding data availability, model interpretability, and clinical implementation. Standardized datasets, external validation, and cross-disciplinary collaborations are crucial for implementing ML innovations in clinical settings. Subsequent investigations should prioritize joint initiatives among data scientists, healthcare providers, and health authorities to translate AI innovations into real-world applications and to enhance the impact of ML on cervical cancer care. By synthesizing recent developments, this review highlights the potential of ML to improve clinical outcomes and shaping the future of cervical cancer management.