@inproceedings{giordano-di-buono-2024-large,
    title = "Large Language Models as Drug Information Providers for Patients",
    author = "Giordano, Luca  and
      di Buono, Maria Pia",
    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-1.7",
    pages = "54--63",
    abstract = "Recently, a significant interest has arisen about the application of Large Language Models (LLMs) in medical settings to enhance various aspects of healthcare. Particularly, the application of such models to improve knowledge access for both clinicians and patients seems very promising but still far from perfect. In this paper, we present a preliminary evaluation of LLMs as drug information providers to support patients in drug administration. We focus on posology, namely dosage quantity and prescription, contraindications and adverse drug reactions and run an experiment on the Italian language to assess both the trustworthiness of the outputs and their readability. The results show that different types of errors affect the LLM answers. In some cases, the model does not recognize the drug name, due to the presence of synonymous words, or it provides untrustworthy information, caused by intrinsic hallucinations. Overall, the complexity of the language is lower and this could contribute to make medical information more accessible to lay people.",
}

Recently, a significant interest has arisen about the application of Large Language Models (LLMs) in medical settings to enhance various aspects of healthcare. Particularly, the application of such models to improve knowledge access for both clinicians and patients seems very promising but still far from perfect. In this paper, we present a preliminary evaluation of LLMs as drug information providers to support patients in drug administration. We focus on posology, namely dosage quantity and prescription, contraindications and adverse drug reactions and run an experiment on the Italian language to assess both the trustworthiness of the outputs and their readability. The results show that different types of errors affect the LLM answers. In some cases, the model does not recognize the drug name, due to the presence of synonymous words, or it provides untrustworthy information, caused by intrinsic hallucinations. Overall, the complexity of the language is lower and this could contribute to make medical information more accessible to lay people.
Giordano, Luca di Buono, Maria Pia
Large Language Models as Drug Information Providers for Patients