Cervical cancer demystified: exploring epidemiology, risk factors, screening, treatment modalities, preventive measures, and the role of artificial intelligence

Cervical cancer remains a significant global health challenge, particularly in low- and middle-income countries where access to preventive measures and early detection is limited. This review aims to summarize current evidence on cervical cancer prevention, screening, and treatment, emphasizing the emerging role of artificial intelligence (AI) in improving diagnostic and therapeutic approaches. Material and Methods.A comprehensive literature review was conducted using peer-reviewed articles from Google Scholar, PubMed, and Scopus. The search focused on human papillomavirus (HPV) vaccination, cervical cancer screening methods, treatment strategies, and AI applications in cervical cancer management. Results. Despite advancements in prevention and early detection, cervical cancer remains a leading cause of cancer-related mortality in women, particularly in underdeveloped regions. Screening methods such as Pap smears, HPV DNA testing, and visual inspection with acetic acid (VIA) have been instrumental in reducing incidence rates in developed countries. Additionally, HPV vaccination programs have significantly decreased the prevalence of high-risk HPV infections, contributing to a decline in cervical cancer cases. However, disparities persist due to limited healthcare infrastructure and access to routine screening. AI-driven technologies, including deep learning algorithms and machine learning models, are emerging as valuable tools in cervical cancer detection, risk assessment, and treatment planning. These innovations have the potential to enhance diagnostic accuracy, reduce human error, and improve patient outcomes, particularly in resource-limited settings. Conclusion. The integration of HPV vaccination, advanced screening techniques, and AI-based diagnostic tools represents a transformative approach to cervical cancer prevention and management. Addressing healthcare disparities through the implementation of these innovations is essential for achieving equitable global cervical cancer control. Future research should focus on optimizing AI applications for broader clinical use and ensuring accessibility in low-resource regions.

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