Abstract: OBJECTIVE： To investigate the value of an artificial intelligence (AI) assistant system in cytological diagnosis of cervical glandular epithelial lesions. METHODS： A total of 143 liquid-based thin layer cervical cytological smears diagnosed as atypical adenocyte (AGC) and 631 negative smears were collected. AI assistant system and intermediate pathologist diagnosis were performed on all smears. The diagnostic results of senior physicians were used as the gold standard for comparative analysis，and specificity，sensitivity and other indicators were statistically analyzed. RESULTS： The positive rate and specificity of AI-assisted diagnosis system were 15.7% and 99.8%，while those of intermediate pathologist diagnosis were 18.3% and 99.2% respectively. There was no significant difference between the two groups (P>0.05). The accuracy and sensitivity of AI-assisted system were 97.0% and 84.6%，and the accuracy and sensitivity of intermediate pathologist diagnosis were 99.2% and 99.3%，and the difference between the two groups was statistically significant (P<0.05). The area under ROC curve (AUC) of AI-assisted diagnosis was 0.922，which was lower than that of intermediate pathologist diagnosis (0.993)，and the difference was statistically significant (P<0.05). In addition，the diagnostic agreement rate between AI-assisted diagnosis and intermediate pathologist diagnosis reached 99%，and the corresponding Kappa value was 0.888，indicating that the two diagnostic methods were basically consistent. CONCLUSION： The AI-assisted system has high specificity in the diagnosis of cervical glandular epithelial lesions，but its sensitivity is low，and there is a certain risk of missed diagnosis. Although the accuracy of AI-assisted diagnosis is not as good as that of intermediate pathologist diagnosis，it still had a high diagnostic value. Therefore，the AI assistant system should be further improved and optimized in the diagnostic application of cervical glandular epithelial cells.

Key words: artificial intelligence, thinprep cytologic test, assisted reading, glandular epithelial lesions