Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and barriers to clinical implementation

Cervical cancer faces significant pathological diagnosis challenges including pathologist shortages, subjective interpretation, and inconsistent detection rates. This systematic review evaluates AI technology's application status, development level, and key challenges in cervical cancer pathological diagnosis.

A systematic literature review across three databases (PubMed/MEDLINE, Scopus, Web of Science) covering January 2015 to August 2025. Search terms included "artificial intelligence," "cervical cancer," "pathological diagnosis," "histopathology," "machine learning," and "deep learning." Studies involving AI applications in cervical cancer pathological diagnosis were included, encompassing histopathological, immunohistochemical, and molecular pathological diagnoses. Animal studies, cytological screening, and genomic analyses unrelated to pathological diagnosis were excluded.

From 1,847 identified articles, 56 studies were included. AI technology demonstrated substantial potential in histopathological image analysis, diagnostic support systems, and accuracy validation. Deep learning architectures, particularly convolutional neural networks, achieved 92-98% diagnostic accuracy while reducing processing time from 8-15 minutes to 1-3 minutes per case. However, significant implementation challenges persist including standardization issues, limited clinical validation, and substantial infrastructure costs.

AI technology shows broad application prospects in cervical cancer pathological diagnosis, potentially alleviating pathologist shortages and improving diagnostic standardization. The technology particularly suits cervical cancer prevention in resource-limited regions, supporting global elimination goals, though standardization and validation challenges require addressing before widespread clinical implementation.