AI-Driven Predictive Models for Risk Assessment in Estrogen and Progesterone-Linked Gynaecological Cancers

Gynaecological cancers driven by estrogen and progesterone, including ovarian, endometrial, and certain types of cervical cancers, present significant challenges in early diagnosis and risk assessment. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a transformative tool for predicting cancer risk, identifying high-risk individuals, and improving personalized prevention strategies. This manuscript explores the role of AI-driven predictive models in assessing the risk of estrogen and progesterone-linked gynaecological cancers. It examines current AI methodologies, their applications, and integration into clinical workflows, while also addressing challenges such as data bias, interpretability, and ethical considerations. The paper highlights the future potential of AI in refining cancer risk assessment and preventive oncology.