Full
Outline
To compare the accuracy of multiple large language models (LLM) and manual judgment in predicting infectious disease trends.
Using monthly report data of 17 infectious diseases in Guangzhou from January to December 2024, we used five LLM and expert judgments from public health physicians for prediction and compared the predicted results with actual trends.
A total of 99 predictions were conducted for 17 infectious diseases. The accuracy rate of prediction by public health physicians was 84.85%, and that of the AI-integrated prediction was 73.74%. The prediction accuracy of AI products followed the trend of Kimi (70.71%), Doubao (68.69%), Lingxi (68.69%, parallel), ERNIE Bot (65.66%), and Deep Seek (63.64%). On the basis of 99 referred data items related to dengue fever, norovirus infection, hand-foot-mouth disease, pertussis, and influenza, in the identification of epidemic inflection points, public health physicians showed the accuracy of 50.0%, followed by Kimi (30.0%), AI integration (20.0%), and Doubao (20.0%, parallel); public health physicians showed the F1 score of 0.56, followed by Kimi (0.40), Doubao (0.38), and AI integration (0.27).
Currently, public health physicians have higher accuracy in predicting infectious disease trends than AI, especially in identifying the impacts of complex social factors.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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