ARTIFICIAL INTELLIGENCE IN DIGITAL PATHOLOGY: REAL-WORLD PERFORMANCE EVALUATION IN A CANCER DIAGNOSIS COHORT FROM CHINA

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.