Cohort: 2 

Location: Wuhan, China 

Application Title: Automated Cytology and Telepathology for Population Cervical Cancer Screening 

Application Subtitle: A digital cytology and telepathology system that automates cell analysis and connects clinics to remote specialists, enabling large-scale cervical cancer screening where pathologist capacity is limited 

Team Members: Lan Wang, Xiaorong Sun, Baochuan Pang, Cheng Li and Jian Wang 

The challenge 

Cervical cancer remains one of the leading causes of cancer-related deaths among women in low- and middle-income countries, despite being largely preventable through early detection and treatment. 

A major barrier is the global shortage of trained pathologists. In China, for example, there is an estimated shortage of 100,000 pathologists. This shortage makes it difficult to scale cytology-based screening, which remains one of the most cost-effective and clinically validated methods for cervical cancer detection. Although HPV DNA testing is often promoted as an alternative, it is significantly more expensive and difficult to sustain at population scale in resource-constrained settings. 

Existing efforts to expand screening rely on time-consuming and unstandardized manual cytology review. These conditions are rarely met in rural or underserved areas. As a result, screening coverage remains uneven, diagnoses are often delayed, and health disparities persist. 

The solution 

AI-assisted cytology at population scale 

Landing Med addresses these constraints with an AI-powered cytology platform that automates cell analysis and connects clinics to remote pathologists through a cloud-enabled screening workflow. 

Cytology involves examining cells under a microscope to detect early signs of disease. Landing Med’s platform digitizes this process by combining slide scanning, deep learning–based image analysis and telepathology into a single end-to-end service. The system is designed to operate without requiring extensive local infrastructure, making it suitable for deployment across diverse clinical settings. 

To date, the platform processes approximately 190 terabytes of medical imaging data, supporting high-resolution cytology analysis across large populations. 

How it works 

From sample collection to clinical review 

Partner clinics collect and prepare samples locally or sent to a Landing Med lab. Landing Med’s AI screening system integrates distributed local sample preparation with centralized, AI-driven diagnostic support and analysis, enabling scalable, standardized, and governable cancer screening at population scale.

The system employs clinically validated AI models to perform multi-level risk stratification and pre-screening of digital pathology slides. These models prioritize cases based on diagnostic risk, supporting a structured human–AI collaboration in which cytotechnologists and pathologists focus their expertise where it is most needed. The performance, updates, and clinical alignment of the models are centrally governed through a unified framework, supporting transparency, accountability, and sustained trust across large-scale screening environments.

Supported by an elastic and scalable computing infrastructure, the system is able to dynamically scale to meet the demands of large-scale public health screening programs. Through real-time, intelligent quality control and analytical monitoring, the system ensures consistent diagnostic performance while optimizing the allocation of clinical, technical, and human resources, enabling high-quality screening to be delivered efficiently and sustainably at population scale. 

How it differs from conventional approaches 

Reducing dependence on manual review 

Conventional cytology screening depends heavily on manual slide review and limited specialist capacity. Landing Med’s approach uses AI to triage large volumes of samples, reducing bottlenecks without removing clinical oversight. 

By integrating AI-driven analysis, telepathology and deployment infrastructure into a single platform, the system supports coordinated screening across clinics, hospitals and laboratories. This reduces fragmentation and allows screening programmes to scale more consistently. 

The model has already been deployed across more than 2,000 hospitals and clinics in China and can be adapted to other low-resource settings. 

Impact 

• More than 13 million AI-assisted screenings conducted since 2017 
• 91% coverage across Chinese provinces
• Fivefold increase in screening productivity (from 12 to 60 samples per hour) 
• 90.1% sensitivity and 94.8% specificity for CIN2+ detection 
• Up to 45,000 screenings per day at peak capacity 
• 10% random rechecks of negative cases to support quality control 

Why it matters 

Expanding access to early detection 

Landing Med demonstrates how AI can support population-based cervical cancer screening in settings where specialist capacity is limited.

By reducing reliance on manual review while maintaining clinical oversight, the platform enables earlier detection at scale and supports more consistent screening coverage. This approach addresses practical barriers to prevention and contributes to improved outcomes for women in underserved regions.