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AI for Good Innovate for Impact
Use Case 14: AI-Enabled Pathology Model for Precision Diagnosis
and Treatment
Organization: Huawei Technologies Co., Ltd.
Country: China
Contact Person(s):
Primary contact: Gao Hongli gaohongli@ huawei .com
Secondary contact: Wu Xiaomin amy.wuxiaomin@ huawei .com
Any other contacts: Liang Jiani liangjiani@ huawei .com
1 Use Case Summary Table
Item Details
Category Healthcare
Problem According to the latest report released by the World Health Organization
Addressed (WHO)'s International Agency for Research on Cancer (IARC), the global cancer
burden is increasing, with 20 million new cancer cases and 9.7 million deaths
worldwide by 2022 [1]. Early detection, early diagnosis and early treatment are
the key. It is urgent to expand the accessibility of pathology diagnosis, improve
the accuracy of pathology diagnosis and improve the level of pathology diag-
nosis at the grass-roots level.
In China, the pathology sector faces several challenges, including a signifi-
cant shortage of qualified professionals—with fewer than 20,000 registered
pathologists and an estimated gap of 70,000 to 140,000. Pathologists are
unevenly distributed, with 70% concentrated in tertiary hospitals. Addition-
ally, the compliance rate for initial diagnoses in primary hospitals is low, with
only 13% showing complete compliance and around 30% achieving general
diagnostic agreement.
Key Aspects of Huawei’s RuiPath model uses AI to analyze pathology slides, enabling faster
Solution and more accurate diagnosis across 11 subspecialties. The solution supports
whole slide image analysis, clinical integration and reasoning-based diagnosis,
reducing diagnosis time by 75% and improving early detection. From tradi-
tional microscopes to digitalization to intelligence, AI technology is used to
achieve intelligent consultation and precise screening, improve the early diag-
nosis rate and treatment efficiency of common diseases, reduce misdiagnosis
rates, help make up for the shortage of medical resources, align with interna-
tional standards, and improve global public health standards.
Technology Multimodal Large Language Model for Pathology; Self-supervised Learning;
Keywords Clinical Decision Support Agent
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