<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<ArticleSet>
<Article>
<Journal>
<PublisherName>International Academy of Ecology and Environmental Sciences</PublisherName>
<JournalTitle>Network Biology</JournalTitle>
<eissn>2220-8879</eissn>
<Volume>16</Volume>
<Issue>3</Issue>
<PubDate PubStatus="ppublish">
<Year>2026</Year>
<Month>9</Month>
<Day>1</Day>
</PubDate>
</Journal>
<ArticleTitle>MR PHARMACIST: AI-Based medication consultation platform</ArticleTitle>
<Pages>268-402</Pages>
<Language>EN</Language>
<AuthorList>
<Author>WenJun Zhang</Author>
</AuthorList>
<ArticleList>
<ArticleId IdType="url">http://www.iaees.org/publications/journals/nb/articles/2026-16(3)/MR-PHARMACIST-AI-Based-medication-consultation-platform.pdf</ArticleId>>
</ArticleList>
<Abstract>
In present study an AI-based medication consultation platform, MR PHARMACIST, was developed. It is an advanced web-based platform designed to provide professional medication consultation services using artificial intelligence. MR PHARMACIST collects essential health information including age, gender, symptoms, medical history, current medications, lifestyle, and additional questions. Users can upload text-based medical records (txt, docx, pdf) to enhance the AI's understanding of their condition. It leverages multiple AI models (DeepSeek, GPT, and Gemini) to generate comprehensive, evidence-based medication reports based on patient inputs. The platform supports multilingual interfaces (30+ languages). The platform is built as a single-page application (SPA) using HTML, CSS, and JavaScript, with backend integration via AI APIs. It uses local storage for language preferences and handles file uploads client-side for privacy. The frontend is responsive and accessible, ensuring usability across devices. The potential users of MR PHARMACIST are patients seeking quick, AI-assisted medication advice, healthcare professionals for preliminary consultations, and non-native English speakers, thanks to extensive language support. Full codes, algorithmic description, and user guide were provided.
</Abstract>
</Article>
</ArticleSet>
