AI-Driven predictive modeling of cervical intraepithelial neoplasia severity: a comprehensive analysis with clinical adoption frameworks.

Cervical intraepithelial neoplasia (CIN), a precursor to invasive cervical cancer, requires effective stratification to ensure timely interventions and reduce overtreatment. Traditional screening methods often struggle to address the complex interplay of factors influencing CIN progression. This study adopts AI-driven approaches, including advanced machine learning (ML) models such as Support Vector Machines (SVM) and Neural Networks (NN), along with deep learning techniques, to enhance the prediction of CIN severity. By integrating demographic, lifestyle, reproductive, and virological data, the models provide a comprehensive framework for assessing disease progression with greater precision than conventional methods. The results demonstrate strong predictive performance, with the best models achieving high AUC (Area Under the Curve) and recall values on a separate, temporally distinct hold-out validation set. This robust performance supports the development of personalized screening strategies. These insights underscore the feasibility and systemic benefits of integrating such tools into routine clinical practice, paving the way for more patient-centered care and efficient resource allocation in gynecologic oncology. This study contributes to advancing the adoption of Artificial Intelligence (AI) in healthcare, offering a transformative approach to managing CIN and preventing cervical cancer.