Leveraging AI and data science across the cervical cancer care continuum in developing economies

Cervical cancer remains a significant public health challenge, particularly in developing economies, where late diagnosis and limited access to advanced medical care contribute to high mortality rates. Early detection, accurate diagnosis, and effective management are crucial for improving patient outcomes. In recent years, Artificial Intelligence (AI) and Data Science (DS) have emerged as transformative tools in cervical cancer care, with applications in screening, diagnosis, treatment planning, patient management, and drug discovery. However, these technologies have not been fully leveraged in resource-limited settings. This review systematically analysed 40 peer-reviewed studies, digital tools, and mobile applications published between 2010 and 2025 to assess how AI is being applied across various stages of cervical cancer management. Studies were identified through structured searches in PubMed, Google Scholar, IEEE, Scopus, and ScienceDirect, and data were extracted on use cases, model types, datasets, and performance metrics. The findings reveal that Convolutional Neural Networks (CNNs) dominate image-based diagnostic tasks, while Support Vector Machines (SVMs), Decision Trees, and Random Forests are frequently applied in structured data analysis. NLP techniques are emerging for public engagement and symptom surveillance. Most models demonstrated strong performance, with CNN-based tools achieving up to 98% accuracy in Pap smear classification. However, disparities in AI adoption persist, with high-income countries leading in precision diagnostics and low-resource regions lagging due to infrastructural, regulatory, and data limitations. Notably, few studies have addressed real-world deployment challenges, and recent advances, such as explainable AI (XAI), federated learning, and multimodal approaches, remain underrepresented in this context. Our review recommends a shift toward equitable AI development, utilising open-access datasets, investing in digital infrastructure, providing interdisciplinary training, and establishing ethical frameworks. In conclusion, while AI offers immense potential to revolutionise cervical cancer care, realising this promise requires inclusive, context-aware innovation that addresses both technological and systemic barriers. Bridging these gaps is essential to ensure that advancements in AI benefit underserved populations and contribute meaningfully to global cervical cancer control efforts.