Role of Artificial Intelligence in Cervical Cancer Detection

Cervical cancer remains a significant global health concern, particularly in low- and middle-income countries where access to expert diagnostic services is limited. The integration of artificial intelligence (AI), especially deep learning techniques, into cervical cancer screening has shown promise in enhancing diagnostic accuracy and efficiency. This paper explores the role of AI in cervical cancer detection, focusing on the analysis of colposcopic images. We discuss the critical steps involved, including image acquisition, preprocessing, segmentation, and classification, highlighting the importance of each in the AI diagnostic pipeline. Deep learning models, such as convolutional neural networks (CNNs), have demonstrated high accuracy in classifying cervical lesions, often matching or surpassing the performance of experienced colposcopists. The incorporation of multimodal data—combining colposcopic images with clinical information like patient history and cytology results—further enhances diagnostic precision. Studies have reported that AI-assisted colposcopy can achieve diagnostic accuracies exceeding 90%, underscoring its potential as a valuable tool in cervical cancer screening programs. The integration of AI into cervical cancer detection workflows holds the potential to improve early diagnosis, optimize resource utilization, and ultimately reduce the global burden of cervical cancer.