Building Certified Medical Chatbots: Overcoming Unstructured Data Limitations with Modular RAG

Abstract

Creating a certified conversational agent poses several issues. The need to manage fine-grained information delivery and the necessity to provide reliable medical information requires a notable effort, especially in dataset preparation. In this paper, we investigate the challenges of building a certified medical chatbot in Italian that provides information about pregnancy and early childhood. We show some negative initial results regarding the possibility of creating a certified conversational agent within the RASA framework starting from unstructured data. Finally, we propose a modular RAG model to implement a Large Language Model in a certified context, overcoming data limitations and enabling data collection on actual conversations.

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