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Cytopathological examination plays a crucial role in cancer diagnosis as it reflects the cellular pathology of cancer. However, this process traditionally relies on the visual examination by cytopathologists. Recent advancements in computer and digital imaging technologies have enabled the application of artificial intelligence (AI)‐based models to identify tumor cells in images, thereby assisting cytopathologists in achieving enhanced performance. AI‐based models can improve the accuracy and reproducibility of image evaluation and streamline clinical workflows. Moreover, AI‐based models can analyze a diverse range of sample types, including peripheral blood, urine, ascites, and bone marrow. AI‐based cytopathological recognition can help clinicians screen and diagnose cancer, predict prognosis and recurrence of cancers, such as leukemia, cervical cancer, urothelial carcinoma, and gastric cancer. Additionally, AI‐based models can predict the types of mutations in leukemia. A growing number of studies emphasize the potential of computational image analysis and deep learning‐based AI to build novel diagnostic tools that are conducive to the biomedical field. This review describes the recent developments in AI‐based cytopathological recognition and offers a perspective on how AI tools of cytopathology can help improve cancer diagnosis and prognosis prediction. Future developments in AI model applications can further contribute to the improvement of human health.

Objective: To study the effect of exfoliative cytology combined with CEA, CA125, CA15-3 and CA19-9 examinations on the diagnosis of malignant serous effusion.Methods: 236 cases of patients who were diagnosed as serous effusion during inpatient in our hospital from January of 2015 to January of 2017 were selected as research objects. According to biopsy and pathological examinations, the diagnostic results were cleared that there were 136 cases of patients with benign serous effusion (benign group) and 100 cases of patients with malignant serous effusion (malignant group). Two groups of patients were both given exfoliative cytology combined with CEA, CA125, CA15-3 and CA19-9 examinations for tumor markers to analyze the effect of exfoliative cytology combined with CEA, CA125, CA15-3 and CA19-9 examinations on the diagnosis of malignant serous effusion.Results: The levels of tumor markers CEA, CA125, CA15-3 and CA19-9 in the benign group were all lower than those in the malignant group, and the difference was of statistical significance (p < .05). In addition, exfoliative cytology combined with CEA, CA125, CA15-3 and CA19-9 examinations showed a higher sensitivity, specificity and accuracy than any of the above examinations alone, and the difference was of statistical significance (p < .05).Conclusions: Exfoliative cytology combined with CEA, CA125, CA15-3 and CA19-9 examinations can effectively improve the diagnostic accuracy of malignant serous effusion and is worthy of being spread clinically.

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