What type of study is this?

This is a Systematic Review study.

September 29, 2025Open Access

A Systematic Review of the Application of Artificial Intelligence in Colposcopy: Diagnostic Accuracy for Cervical Intraepithelial Neoplasia and Cervical Cancer

Key Points

AI systems exhibited superior diagnostic accuracy for cervical abnormalities, improving early detection of high-risk lesions.
The review analyzed 19 studies, highlighting AI's increased sensitivity and specificity in cervical cancer diagnosis.
Deep-learning models, specifically convolutional neural networks, reduced diagnostic variability compared to conventional methods.
Standardized datasets and validation in diverse populations are crucial for fully harnessing AI’s potential in healthcare.

Abstract

Background: Artificial intelligence (AI) is increasingly applied to colposcopy to enhance the detection of cervical intraepithelial neoplasia (CIN) and cervical cancer. We conducted a systematic review to summarize the diagnostic performance achieved by AI‑based colposcopic systems. Methods: Following the PRISMA 2020 guidelines, the PubMed database was searched using the search terms ‘artificial intelligence’ and ‘colposcop*’ for articles published between 2019 and 2024. From the initial 43 articles retrieved, 19 studies were selected based on specific inclusion criteria: original research articles, written in the English language, and relevant to CIN or cervical cancer diagnosis. For each, we extracted the sample size, AI architecture (e.g., convolutional neural networks, U-Net/DeepLab V3 + segmentation models, multimodal fusion networks), reference standard, and reported metrics (sensitivity, specificity, accuracy, and area under the curve). Results: Across multiple studies, AI systems demonstrated superior diagnostic accuracy, sensitivity, and specificity, particularly for early detection of high-risk lesions and classification of cervical abnormalities. Deep-learning models, such as convolutional neural networks, consistently outperformed conventional methods by reducing diagnostic variability and offering robust performance even in low-resource settings. The review also highlights the potential of AI for real-time diagnostics and its capacity to support clinical decision-making via automated systems. Conclusion: AI has the potential to revolutionize cervical cancer diagnosis and management by enhancing the accuracy and efficiency of colposcopic evaluations. However, challenges remain, including the development of standardized datasets, validation in diverse populations, and ethical considerations surrounding data privacy and access to technology. Continued research and development are crucial to harness AI’s global potential to improve patient outcomes.

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