# Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics

## TL;DR

This paper reviews how AI, especially deep learning, is being used to detect cervical cancer early by analyzing cell images, and identifies trends and limitations in current research.

## Contribution

The study systematically reviews recent deep learning models, datasets, and metrics in cervical cytology AI research, highlighting trends and methodological gaps.

## Key findings

- Hybrid models and Vision Transformers are increasingly used in cervical cytology AI.
- SIPaKMeD and Herlev are the most common datasets, but private datasets are rising in use.
- Most models report high accuracy, but limitations like poor cross-validation and inconsistent criteria remain.

## Abstract

The use of artificial intelligence (AI) in cervical cytology has increased substantially due to the need for automated tools that support the early detection of precancerous lesions.

This systematic review examined deep learning models applied to cervical cytology images, focusing on the architectures used, the datasets employed, and the performance metrics reported. Articles published between 2022 and 2025 were retrieved from Scopus using PRISMA methodology. After applying inclusion criteria and full-text screening, 77 studies were included for RQ1 (models), 75 for RQ2 (datasets), and 71 for RQ3 (metrics).

Hybrid models were the most prevalent (56%), followed by convolutional neural networks (CNNs) and a growing number of Vision Transformer (ViT)-based approaches. SIPaKMeD and Herlev were the most frequently used datasets, although the use of private datasets is increasing. Accuracy was the most commonly reported metric (mean 87.76%), followed by precision, recall, and F1-score. Several hybrid and ViT-based models exceeded 92% accuracy. Identified limitations included limited cross-validation, reduced clinical representativeness of datasets, and inconsistent diagnostic criteria.

This review synthesizes current trends in AI-based cervical cytology, highlights common methodological limitations, and proposes directions for future research to enhance clinical applicability and standardization.