@inproceedings{sanna-etal-2024-building,
    title = "Building Certified Medical Chatbots: Overcoming Unstructured Data Limitations with Modular {RAG}",
    author = "Sanna, Leonardo  and
      Bellan, Patrizio  and
      Magnolini, Simone  and
      Segala, Marina  and
      Ghanbari Haez, Saba  and
      Consolandi, Monica  and
      Dragoni, Mauro",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Thompson, Paul  and
      Ondov, Brian",
    booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cl4health-1.15",
    pages = "124--130",
    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.",
}

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.
Sanna, Leonardo Bellan, Patrizio Magnolini, Simone Segala, Marina Ghanbari Haez, Saba Consol i, Monica Dragoni, Mauro
Building Certified Medical Chatbots: Overcoming Unstructured Data Limitations with Modular RAG