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生成式人工智能GPT-4驱动的中药处方生成研究
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| 篇名： | 生成式人工智能GPT-4驱动的中药处方生成研究 |
| TITLE： | Generation of traditional Chinese medicine prescription driven by generative artificial intelligence GPT-4 |
| 摘要： | 目的 评估生成式人工智能（AIGC）中的GPT-4模型生成中药处方的安全性、适宜性，为AIGC赋能中医药行业提供研究思路。方法将2020年版《中国药典》和第5版《中药学》作为语料，由GPT-4及基于GPT-4开发的实时联网模型（简称“联网模型”）对其进行深度学习。人工抽取近几年中医药类专家共识收录的临床案例，由GPT-4模型和联网模型根据诊断重新生成处方。由中医药学专家对GPT-4生成处方、联网模型生成处方以及专家共识处方进行盲评打分，同时通过图灵测试来评估GPT-4模型和联网模型是否具有与人类智能相当的能力。结果GPT-4模型生成的中药处方的平均分与人工处方比较，差异无统计学意义（P＞0.05）；联网模型生成处方的平均分与GPT-4模型生成的中药处方比较，差异无统计学意义（P＞0.05）。模型生成处方在图灵测试中被误判为人工处方的占比达51.11%。结论GPT-4模型生成的中药处方在安全性、适宜性方面已经具备一定的水平，且GPT-4模型通过了所设置的图灵测试；在诊疗过程中引入AIGC可能为临床中药的合理使用提供技术支撑。 |
| ABSTRACT： | OBJECTIVE To evaluate the safety and suitability of traditional Chinese medicine prescriptions generated by generative artificial intelligence （AIGC）， and to provide research ideas for empowering the traditional Chinese medicine industry with AIGC. METHODS Using the 2020 edition of Chinese Pharmacopoeia and the 5th edition of Traditional Chinese Medicine as corpus， GPT-4 and the real-time networking model developed based on GPT-4 （referred to as the “networking model”） were used for deep learning. The clinical cases included in the consensus of traditional Chinese medicine experts in recent years were extracted manually to regenerate prescriptions based on diagnosis using the GPT-4 model and networking model； traditional Chinese medicine experts conducted blind evaluation and scoring of GPT-4 generated prescriptions， networking model generated prescriptions， and expert consensus prescriptions. At the same time， Turing testing was used to evaluate whether the GPT-4 model and networking model had the same ability as human intelligence. RESULTS The average score of traditional Chinese medicine prescriptions generated by the GPT-4 model showed no statistically significant difference compared to manual prescriptions （P＞0.05）， while the average score of prescriptions generated by the networking model showed no statistically significant difference compared to traditional Chinese medicine prescriptions generated by the GPT-4 model （P＞0.05）. The proportion of model-generated prescriptions mistakenly judged as manual prescriptions in the Turing test was 51.11%. CONCLUSIONS The traditional Chinese medicine prescriptions generated by the GPT-4 model have reached a certain level of safety and suitability， and the GPT-4 model has passed the Turing test. The introduction of AIGC in the diagnosis and treatment process may provide technical support for the rational use of clinical traditional Chinese medicine. |
| 期刊： | 2023年第34卷第23期 |
| 作者： | 陈祺焘；倪璟雯；徐君；高晓涵；夏丽珍 |
| AUTHORS： | CHEN Qitao，NI Jingwen，XU Jun，GAO Xiaohan，XIA Lizhen |
| 关键字： | GPT-4；中药处方；生成式人工智能 |
| KEYWORDS： | GPT-4； traditional Chinese medicine prescription； generative artificial intelligence |
| 阅读数： | 91 次 |
| 本月下载数： | 8 次 |
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