AI-Enhanced Pharmacy Inventory Management System

Abstract

Pharmacy inventory systems face persistent issues like drug overstocking, shortages, manual inefficiencies, and lack of intelligent decision support. This paper presents an AI-driven, modular pharmacy inventory management framework that leverages machine learning, natural language processing (NLP), and vector-based search to optimize stock control, medicine classification, and real-time user interaction. The system integrates ABC-VED inventory prioritization, RAG-based drug substitution, and a biomedical chatbot using BioBERT and Gemini API to offer a comprehensive, contextaware solution. The architecture combines analytics with deep learning models for classification and recommendations for ABC-VED optimization. BioBERT is fine-tuned for therapeutic categorization and query understanding, while RAG supports rapid semantic search for alternative medications based on chemical and therapeutic similarity. Key challenges include processing unstructured drug descriptions, ensuring chatbot factuality, and managing largescale vector search. Future work will explore real-time inventory forecasting using time-series models, regulatory data integration, and deployment at scale using cloud infrastructure and IoT-based drug tracking.

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