Author(s)

Qi Wang^1,2,3, Jing Xiao¹, ZengYan Li³, TingTing Yu¹, Lin Tian^1*, Jun Shen^2,3*

Affiliation(s)

¹Department of Pathology, Renmin Hospital, Hubei University of Medicine, Shiyan 442000, Hubei, China.

²Diagnosis Teaching and Research Section, Renmin Hospital, Hubei University of Medicine, Shiyan 442000, Hubei, China.

³The Third Clinical College, Renmin Hospital, Hubei University of Medicine, Shiyan 442000, Hubei, China.

Corresponding Author

Lin Tian, Jun Shen

ABSTRACT

This paper focuses on the core technological breakthroughs in the integration of digital pathology slide systems and artificial intelligence (AI), with precision cancer diagnosis as the entry point. It deeply analyzes the innovative applications of multi-scale feature fusion algorithms, self-supervised learning models, and multi-omics integration technologies in clinical practice. Through specific cases such as breast cancer HER2 quantification and lung cancer subtype classification, it elaborates how AI-assisted diagnosis improves the consistency of biomarker detection (HER2 assessment consistency increased from 93% to 99%), enhances diagnostic efficiency (doubling the number of cases processed), and optimizes survival prediction accuracy (C-index increased by an average of 1.1-5.5%). It also analyzes the standardization dilemmas and model generalization challenges in technology implementation, and looks forward to the construction path of a human-machine collaborative diagnosis ecosystem, providing references for the clinical transformation of digital pathology.

KEYWORDS

Digital pathology slides; Artificial intelligence; Cancer diagnosis; Multi-omics integration; Clinical transformation

CITE THIS PAPER

Qi Wang, Jing Xiao, ZengYan Li, TingTing Yu, Lin Tian, Jun Shen. Artificial Intelligence in digital pathology: real-world performance evaluation in a cancer diagnosis cohort from China. World Journal of Educational Studies. 2025, 3(4): 34-39. DOI: https://doi.org/10.61784/wjes3066.

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