In the first half of 2025, a grassroots hospital in the Beijing-Tianjin-Hebei region introduced a highly anticipated medical large model system, aiming to improve the efficiency of electronic medical record generation and provide auxiliary diagnostic services. Although the hospital management was full of expectations for this project, the actual implementation showed results that fell short of expectations, even causing "negative effects." What caused these models, which performed well in top-tier tertiary hospitals, to "fail" in grassroots hospitals?
First, the model faced challenges with dialect recognition. In actual hospital operations, the model failed to correctly understand the local residents' dialects, leading to chaotic medical records that required doctors to spend more time manually correcting them. This issue exposed the limitations of medical large models in language understanding, highlighting that the specific needs of grassroots hospitals were not adequately considered.
Second, insufficient data completeness was also a key factor. Top hospitals had highly structured data following uniform formats, allowing seamless integration between systems. However, the environment in grassroots hospitals was relatively complex, with data often being scattered and unstandardized. This made it difficult for the large model to provide accurate diagnostic results when lacking high-quality input.
Additionally, differences in disease patterns made the application of these models more challenging in grassroots hospitals. Top hospitals mainly dealt with complex and difficult cases, while grassroots hospitals focused on common diseases and chronic disease management. When the original design of the large model was aimed at complex diseases, applying it to common conditions in grassroots hospitals naturally led to misalignment. This mismatch not only reduced diagnostic accuracy but also increased the workload for doctors, forcing them to constantly struggle between the model's suggestions and their own judgments.
Faced with these challenges, hospital administrators realized that the introduced AI large model was not simply an "efficiency tool," but one that needed to align with the actual conditions of grassroots medical environments. In the future, only by fully considering the specific needs, data situations, and disease characteristics of grassroots hospitals can medical AI truly fulfill its intended role.
