Revolutionizing Cervical Cancer Screening with AI

Cervical cancer remains one of the most preventable cancers when detected early, yet traditional cytology workflows are labor-intensive and time-consuming. GBC, in partnership with Danner Lab, has introduced an AI-powered digital pathology platform that automates the entire screening process, improving accuracy and efficiency.

How It Works: Thin-Layer Smears + AI Deep Learning

Using thin-layer smear preparation, cellular debris and background noise are removed, producing a clear, single-cell layer. Each specimen generates 2,300+ high-resolution images, which are analyzed by a deep learning model trained on the Bethesda classification system—the global standard for cervical cytology.

This AI-driven process can automatically classify cells, flagging potential abnormalities for review, and significantly reducing manual workload.

Key Advantages
• Higher Accuracy: AI improves consistency and reduces human error.
• Faster Results: Automated analysis accelerates screening turnaround times.
• Regulatory Compliance: Overcomes limitations such as Taiwan and U.S. regulatory caps of ≤80 slides/day per cytologist.
• Scalable Applications: Supports not only cervical cancer screening but broader digital pathology and drug discovery workflows.

Why It Matters

By combining AI with optimized smear preparation, GBC is reshaping traditional cytology into a high-throughput, precision-driven, and scalable process. This represents a breakthrough for both population-based screening programs and personalized medicine, ensuring patients receive earlier, more accurate results.