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5种中文大语言模型对门诊处方中药物相互作用的审核能力比较
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| 篇名： | 5种中文大语言模型对门诊处方中药物相互作用的审核能力比较 |
| TITLE： | Comparative audit capabilities of five Chinese large language models in detecting drug-drug interaction in outpatient prescriptions |
| 摘要： | 目的 为审方软件的优化及医疗机构合理用药辅助审方工具的选择提供参考。方法收集某三甲医院门诊2025年1月至2026年3月美康合理用药监测系统中符合纳入标准的处方559张。根据建立的复合参照标准（CRS）进行判定，其中阳性[存在药物相互作用（DDI）]处方206张、阴性（不存在DDI）处方353张。分别采用DeepSeek、豆包、文心一言、Kimi和通义千问对559张处方的DDI进行判定，并对其输出的DDI文本进行二值化判定。以CRS判定的结果为基准，构建混淆矩阵并计算模型的性能评价指标；比较任意2种大语言模型间敏感性的差异，计算二者的相对敏感性（RS）及其95%置信区间（CI），并以森林图展示。结果文心一言的敏感性最高（74.8%），漏报风险最低；但其特异性仅为40.8%，且准确性最低（53.3%），存在较高的误报风险。通义千问的准确性最高（65.3%），F1分数最优（0.587）。DeepSeek的敏感性与特异性均居中。豆包与Kimi的敏感性均较低（分别为38.8%、45.1%），漏报风险均较高。两两比较结果显示，文心一言的敏感性显著高于DeepSeek[χ2＝4.84，P＝0.028；RS＝1.124，95%CI（1.018，1.230）]，通义千问的敏感性显著高于豆包[χ2＝4.77，P＝0.029；RS＝1.239，95%CI（1.035，1.443）]，DeepSeek的敏感性显著高于豆包[χ2＝37.65，P＜0.001；RS＝1.715，95%CI（1.491，1.939）]；Kimi与豆包、通义千问与Kimi的敏感性差异均无统计学意义（P＞0.05）。结论文心一言适用于对漏报容忍度较低的初步筛查场景，且需联合人工复核；通义千问适合用于门诊辅助审核；DeepSeek可作为无审方软件医疗机构的过渡方案；豆包与Kimi不宜独立用于处方中DDI审核。 |
| ABSTRACT： | OBJECTIVE To provide a reference for the optimization of prescription-review software and the selection of auxiliary prescription-review tools for medical institutions.METHODS A total of 559 eligible prescriptions were collected from the Meikang Prescription Automatic Screening System of a tertiary grade-A hospital from January 2025 to March 2026. Judged according to the composite reference standard （CRS） established in this study， there were 206 positive prescriptions [with drug-drug interaction （DDI）] and 353 negative prescriptions （without DDI）. DeepSeek， Doubao， Wenxin yiyan， Kimi and Tongyi qianwen were separately adopted to identify DDI in the 559 prescriptions， and binary judgment was performed on their output texts related to DDI. Taking the results judged by CRS as the gold standard， the confusion matrix was constructed and model performance indicators were calculated. The differences in sensitivity between any two large language models were compared， the relative sensitivity （RS） and its 95% confidence interval （CI） were calculated， and the results were visualized by a forest plot.RESULTS Wenxin yiyan achieved the highest sensitivity （74.8%） with the lowest risk of missed reports， whereas its specificity was only 40.8%. It had the lowest accuracy （53.3%） and presented a high risk of false positive reports. Tongyi qianwen obtained the highest accuracy （65.3%） and the optimal F1-score （0.587）. DeepSeek showed moderate performance in both sensitivity and specificity. Doubao and Kimi exhibited relatively low sensitivity （38.8% and 45.1%， respectively）， accompanied by high risks of missed reports. Pairwise comparison revealed that the sensitivity of Wenxin yiyan was significantly higher than that of DeepSeek [ χ 2 ＝4.84， P ＝0.028； RS＝1.124， 95%CI （1.018， 1.230）]. The sensitivity of Tongyi qianwen was significantly higher than that of Doubao [ χ 2 ＝4.77， P ＝0.029； RS＝1.239， 95%CI （1.035， 1.443）]. DeepSeek had significantly higher sensitivity than Doubao [ χ 2 ＝37.65， P ＜0.001； RS＝1.715， 95%CI （1.491， 1.939）]. No statistically significant differences in sensitivity were observed between Kimi versus Doubao and between Tongyi qianwen versus Kimi （ P ＞0.05）.CONCLUSIONS Wenxin yiyan is applicable for preliminary-screening scenarios with low tolerance for missed reports and requires combined manual review. Tongyi qianwen is suitable for auxiliary outpatient prescription review. DeepSeek can act as a transitional solution for medical institutions without dedicated prescription-review software. Doubao and Kimi are not recommended for independent application in prescription DDI audit. |
| 期刊： | 2026年第37卷第19期 |
| 作者： | 王紫灏；李晋宝；张研；闫森 |
| AUTHORS： | WANG Zihao，LI Jinbao，ZHANG Yan，YAN Sen |
| 关键字： | 大语言模型;药物相互作用;处方审核;敏感性;辅助审方工具;门诊处方 |
| KEYWORDS： | large language models;drug-drug interaction;Prescription review;Sensitivity;auxiliary prescription-review tools;Outpatient prescription |
| 阅读数： | 3 次 |
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