HARNESSING MACHINE LEARNING IN HPV DIAGNOSTICS: MODEL PERFORMANCE, EXPLAINABILITY, AND CLINICAL INTEGRATION

Abstract

: Human Papillomavirus (HPV) remains a significant global health concern, contributing to cervical and oropharyngeal cancers. While traditional diagnostic methods such as PCR-based assays and cytological screenings are widely used, they present limitations in sensitivity, specificity, and scalability. Recent advances in machine learning (ML) have enabled more precise and automated HPV detection and genotyping. This review aims to evaluate the current ML methodologies in HPV diagnostics, compare their performance metrics, and discuss future directions for improving artificial intelligence (AI) -driven HPV screening. CNN-based models exhibited superior performance in cytology and histopathology-based HPV detection, achieving high accuracy in lesion classification. Hybrid models integrating ML with molecular diagnostics improved HPV genotyping precision. Support vector machine (SVM) and random forest (RF) demonstrated efficacy in genomic classification, whereas transformer-based models enhanced feature extraction and risk stratification. Despite these advancements, data heterogeneity, explainability, and clinical validation remain substantial barriers to widespread adoption. ML-driven HPV diagnostics offer unprecedented improvements in efficiency, accuracy, and accessibility. However, critical issues related to data standardization, bias mitigation, and regulatory frameworks must be addressed to ensure clinical reliability. Future research should prioritize explainable AI (XAI), federated learning, and robust validation studies to enhance model generalizability and real-world applicability. The seamless integration of AI-powered tools into HPV screening programs holds transformative potential for early detection, personalized risk assessment, and improved patient outcomes, ultimately contributing to the global reduction of HPV-related malignancies.

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