Construction and efficacy verification of an intelligent pharmaceutical Q&A platform based on AI hallucination-suppression
- VernacularTitle:基于AI幻觉抑制的药学智能问答平台的构建与效能验证
- Author:
Zhengwang WEN
1
;
Jiaying WANG
1
;
Wenyue YANG
1
;
Haoyu YANG
1
;
Xiao MA
2
;
Yun LIU
1
Author Information
1. Dept. of Pharmacy，Handan First Hospital，Hebei Handan 056003，China
2. Neusoft Group Co.，Ltd.，Hebei Handan 056003，China
- Publication Type:Journal Article
- Keywords:
intelligent pharmaceutical Q&A platform;
AI hallucination;
large language models;
DeepSeek;
artificial intelligence
- From:
China Pharmacy
2026;37(2):226-231
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To construct an intelligent pharmaceutical Q&A platform for precision medication with low “artificial intelligence （AI） hallucination”， aiming to enhance the accuracy， consistency， and traceability of medication consultations. METHODS Medication package inserts were batch-processed and converted into structured data through Python programming to build a local pharmaceutical knowledge base. The retrieval and question-answering processes were designed based on large language models， and system integration and localized deployment were completed on Dify platform. By designing typical clinical medication questions and comparing the output of the intelligent pharmaceutical Q&A platform with the online version of DeepSeek across dimensions such as peak time retrieval， half-life， and dosage adjustment reasoning for patients with renal impairment， the accuracy and reliability of its retrieval and reasoning results were evaluated. RESULTS The intelligent pharmaceutical Q&A platform， constructed based on local drug package inserts， achieved 100% accuracy in retrieval and reasoning for peak time， half-life， and dosage adjustment schemes. In comparison， the online version of DeepSeek demonstrated accuracies of 30%（6/20）， 50%（10/20）， and 38%（23/60） across these three dimensions， respectively. CONCLUSIONS The constructed intelligent pharmaceutical Q&A platform is capable of accurately retrieving and extracting information from the local knowledge base based on clinical inquiries， thereby avoiding the occurrence of AI hallucinations and providing reliable medication decision support for healthcare professionals.
