Precision prediction of cervical cancer outcomes: A machine learning approach to recurrence and survival analysis

ABSTRACT

Cervical cancer remains a significant global health challenge, with high rates of recurrence and mortality, particularly in low-resource regions. Effective prediction of recurrence and survival is crucial for optimizing treatment and improving patient outcomes. Recently, artificial intelligence (AI) has emerged as a transformative tool in oncology, providing advanced methodologies for analyzing large-scale medical data and offering predictive insights into patient outcomes. This review comprehensively explores the role of AI in predicting cervical cancer recurrence and survival, focusing on techniques such as machine learning, deep learning, and natural language processing. The integration of AI with medical imaging, genomics, and clinical data is discussed, along with the associated challenges and limitations. Future directions and the potential impact of AI on personalized medicine in cervical cancer care are also examined.