Pharmaceutical ChatBot Assistant Using Generative AI and Knowledge Graph with Specific Pharmaceutical Resources

Pharmaceutical ChatBot Assistant Using Generative AI and Knowledge Graph with Specific Pharmaceutical Resources | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pharmaceutical ChatBot Assistant Using Generative AI and Knowledge Graph with Specific Pharmaceutical Resources BRAHAMI Menaouer, Chalabi Younes, Elouissi Elmehdi Mokhtar, Sabri Mohammed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7743987/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Iran Journal of Computer Science → Version 1 posted 15 You are reading this latest preprint version Abstract Artificial Intelligence (AI) has the potential to revolutionize the medical and pharmaceutical sectors. AI and related technologies can significantly address some supply and demand challenges in the pharmaceutical system, such as pharmaceutical AI assistants, chatbot technology, and PharmaRobots. Generative AI (GenAI) is a type of AI that aims to produce new content rather than merely recognizing it. One of the most significant advances in Natural Language Processing (NLP) in recent years is the development of Large Language Models (LLMs). In this study, we propose a chatbot system to support pharmacists through the development of a pharmaceutical chatbot assistant, called PharmaBot, which is proficient in delivering accurate and contextually relevant responses concerning medications. To this end, we developed a general architectural design that focuses on tailoring LLMs, utilizing Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) to evaluate their performance with specific pharmaceutical resources. A comprehensive knowledge base was constructed by meticulously preprocessing 18,698 pharmaceutical files from the Vidal Group. A key innovation is a dual-embedding strategy that captures both semantic and structural information to facilitate nuanced and context-aware similarity searches. By adopting sophisticated evaluation measures such as ROUGE, BERTScore, METEOR, and Cosine Similarity, the effectiveness of the algorithms used in producing precise and cohesive summaries was evaluated. Based on the aggregated proposals and findings in the existing literature, this paper concludes with a set of challenges and research recommendations, hopefully contributing to guiding research in the extremely active pharmaceutical domain. Knowledge Management Generative AI (GenAI) Retrieval-Augmented Generation (RAG) Knowledge Graphs (KGs) Large Language Models (LLMs) Information Retrieval Pharmaceutical Chatbot Decision Support Systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Iran Journal of Computer Science → Version 1 posted Editorial decision: Revision requested 18 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviews received at journal 17 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers invited by journal 05 Oct, 2025 Editor assigned by journal 05 Oct, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 29 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7743987","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529940707,"identity":"af318926-bf2c-434d-9278-09177706d382","order_by":0,"name":"BRAHAMI 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