Ruibin Agent versus mainstream large language models: A comparative study on medical literature comprehension with esophageal cancer as a case study
- VernacularTitle:睿宾Agent与主流大语言模型医学文献解读能力比较：以食管癌为例
- Author:
Pinghua WEN
1
;
Zhijie JIANG
1
;
Huan JIANG
1
;
Xianglei YUAN
1
;
Yu ZHOU
2
;
Hu MA
3
;
Chao LU
3
;
Bing HU
1
Author Information
1. Department of Gastroenterology and Hepatology, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
2. Shanghai Huihao Yisheng Information Technology Co., Ltd, Shanghai, 200080, P. R. China
3. Huawei Technologies Co., Ltd, Chengdu, 610000, P. R. China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
large language models;
interpretation of medical literature;
esophageal neoplasms
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(10):1404-1410
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the application value of artificial intelligence in medical research assistance, and analyze the key paths to achieve precise execution of model instructions, improvement of model interpretation completeness, and control of hallucinations. Methods Taking esophageal cancer research as the scenario, five types of literature including research articles, case reports, reviews, editorials, and guidelines were selected for model interpretation tests. The model performance was systematically evaluated from five dimensions: recognition accuracy, format accuracy, instruction execution accuracy, content reliability rate, and content completeness index. The performance differences of Ruibin Agent, GPT-4o, Claude 3.7 Sonnet, DeepSeek V3, and DouBao-pro models in medical literature interpretation tasks were compared. Results A total of 15 studies were included, with 3 studies of each type. The five models collectively conducted 1 875 tests. Due to the poor recognition accuracy of the editorial type, the overall recognition accuracy of Ruibin Agent was significantly lower than other models (92.0% vs. 100.0%, P＜0.001). In terms of format accuracy, Ruibin Agent was significantly better than Claude 3.7 Sonnet (98.7% vs. 92.0%, P=0.002) and GPT-4o (98.7% vs. 78.9%, P＜0.001). In terms of instruction execution accuracy, Ruibin Agent was better than GPT-4o (97.3% vs. 80.0%, P＜0.001). In terms of content reliability rate, Ruibin Agent was significantly lower than Claude 3.7 Sonnet (84.0% vs. 92.0%, P=0.010) and DeepSeek V3 (84.0% vs. 94.7%, P＜0.001). In terms of content completeness index, the median scores of Ruibin Agent, GPT-4o, Claude 3.7 Sonnet, DeepSeek V3, and DouBao-pro were 0.71, 0.60, 0.85, 0.74, and 0.77, respectively. Conclusion Ruibin Agent has significant advantages in terms of formatted interpretation of medical literature and instruction execution accuracy. In the future, it is necessary to focus on optimizing the recognition ability of editorial types, strengthening the coverage ability of core elements of various types of literature to improve interpretation completeness, and improving content reliability through optimizing the confidence mechanism to ensure the rigor of medical literature interpretation.
