Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection

Artificial intelligence (AI) is reshaping cervical cancer screening by automating interpretation of cytology, colposcopic, and related imaging to improve early detection, especially in low- and middle-income countries. This review synthesizes advances in preprocessing; segmentation; representation learning; and supervised, semi-supervised, unsupervised, and transformer-based models, with emphasis on multimodal fusion with HPV testing, spectroscopy, and MRI. Across retrospective datasets and growing real-world deployments, AI systems can achieve high accuracy and sensitivity, accelerate workflows, reduce costs, and expand coverage via portable and edge-computing devices. However, translation is constrained by data bias, variable image quality, opaque decision-making, and fragmented regulation. We outline requirements for clinically robust and equitable deployment, including diverse multi-center datasets, federated and privacy-preserving learning, explainable interfaces, standardized validation with histopathologic endpoints, and clinician-in-the-loop workflows. Finally, we highlight future directions such as hybrid explainable AI with large language models, multi-omics integration, and adaptive models resilient to data drift.