A HYBRID AI CHATBOT FRAMEWORK FOR INTELLIGENT PHARMACY MANAGEMENT SYSTEMS

Abstract

The evolution of digital healthcare demands a shift in Pharmacy Management Systems (PMS) from static inventory tools to intelligent, user-centric platforms. Existing PMS solutions are often hampered by rigid interfaces, limited decision support, and inefficient handling of complex queries. While some integrate basic chatbots, these are typically rule-based, lacking the capacity for nuanced understanding or adaptive interaction. This research proposes a dual-stage AI-powered PMS that integrates a smart chatbot to enhance efficiency, accuracy, and user experience. The first stage employs NLP-driven SQL query generation for fast, structured data retrieval (e.g., drug availability, expiry dates). For complex, unstructured queries requiring clinical reasoning, the system leverages a transformer-based model (e.g., GPT) to deliver context-aware responses. This hybrid architecture reduces computational overhead by offloading routine tasks to lightweight processes while preserving the capability for advanced interaction. Designed with modularity and scalability in mind, the system supports future extensions such as multilingual interfaces, voice input, and image-based drug recognition—positioning it as a robust, adaptive solution for next-generation pharmacy management.