Pre-analytical reporting in AI-assisted cervical cytology: a scoping review of data acquisition documentation.

Artificial intelligence (AI) models for cervical cytology screening have achieved pooled accuracy and sensitivity values exceeding 90% in recent meta-analyses, and several commercial systems are now in clinical use. However, whether these results generalize across laboratories, scanners, and clinical settings depends on pre-analytical factors-sample preparation, staining, digitization, and annotation-that are known to introduce substantial variability into the data that models consume. This scoping review assessed how consistently these factors are documented in the cervical cytology AI literature. We examined 28 datasets published between 2005 and 2025, extracting information on 16 pre-analytical variables spanning sample preparation, digitization, and annotation. The mean reporting completeness was 11.4 out of 16 variables (71.2%). Digitization was the weakest category (mean 60.2%), with scanning mode unreported in 57.1% of datasets, image file format in 60.7%, and color normalization status in 78.6%. Staining protocol was mentioned by 75.0% of datasets but described in sufficient procedural detail by only 7.1%. Quantitative inter-annotator agreement was provided by 14.3% of datasets, despite well-documented inter-observer variability in cervical cytology. Notably, the variables with the lowest reporting rates correspond to those identified in the digital pathology literature as the most significant sources of AI model performance variability. To address this gap, we propose PRECY-AI (Pre-analytical Reporting Checklist for Cervical Cytology AI), a 16-item checklist of essential and recommended reporting items designed to complement existing general-purpose guidelines such as TRIPOD+AI and CLAIM. Adoption of domain-specific pre-analytical reporting standards could improve the reproducibility, comparability, and clinical translatability of cervical cytology AI research.