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Updated: Feb 6, 2026
05:33
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
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Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and
Yue Zhang1, Jiangbo Yuan2, Lin Chen1
1Shaanxi Provincial People's Hospital, Xi'an, China.
Frontiers in Oncology
|February 5, 2026
Summary
Artificial intelligence (AI) shows promise in improving cervical cancer pathological diagnosis, enhancing accuracy and speed. However, standardization and clinical validation challenges must be addressed for widespread adoption.
Area of Science:
- Medical Informatics
- Oncology
- Pathology
Background:
- Cervical cancer pathological diagnosis faces challenges like pathologist shortages and subjective interpretation.
- Inconsistent detection rates impact diagnostic reliability and patient outcomes.
Purpose of the Study:
- To systematically review the application status and development of AI in cervical cancer pathological diagnosis.
- To identify key challenges and prospects of AI technology in this field.
Main Methods:
- Systematic literature review of studies from January 2015 to August 2025.
- Searched PubMed/MEDLINE, Scopus, and Web of Science using terms like 'artificial intelligence,' 'cervical cancer,' and 'pathological diagnosis.'
- Included studies on AI in histopathological, immunohistochemical, and molecular pathological diagnoses; excluded animal studies and unrelated genomic analyses.
Main Results:
- 56 studies out of 1,847 identified were included, highlighting AI's potential in image analysis and diagnostic support.
- Deep learning models, particularly convolutional neural networks, achieved 92-98% accuracy and reduced processing time significantly.
- Implementation challenges include standardization, limited clinical validation, and high infrastructure costs.
Conclusions:
- AI offers significant potential to address pathologist shortages and improve diagnostic standardization in cervical cancer.
- The technology is particularly suited for resource-limited settings, aiding global elimination goals.
- Widespread clinical implementation requires overcoming standardization and validation hurdles.
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