A research team led by The Hong Kong University of Science and Technology (HKUST) has developed a pioneering artificial intelligence (AI) pathology analysis system that can accurately recognize multiple types of cancer using only a minimal number of samples, without requiring any additional training.
This breakthrough significantly enhances the flexibility and efficiency of AI-assisted medical care, marking a major step forward toward the widespread adoption of intelligent pathology.
The system is the first to introduce the concept of "in-context learning" from natural language processing into pathological image analysis.
Functioning as a "plug-and-play" intelligent diagnostic tool, PRET fundamentally overcomes the need for task-specific fine-tuning in traditional AI models.
The research team conducted comprehensive validation of the PRET system using 23 international benchmark datasets from medical institutions in the Chinese Mainland, the United States, and the Netherlands, covering 18 cancer types and various diagnostic tasks.
The results showed that the system outperformed existing methods in 20 tasks, with its Area Under the Curve (AUC)—a measure of diagnostic accuracy—exceeding 97% in 15 of those tasks.
【MORE】