DART: A Structured Dataset of Regulatory Drug Documents in Italian for Clinical NLP

• URL: http://arxiv.org/abs/2510.18475v1
• Date: Tue, 21 Oct 2025 09:53:17 GMT
• Title: DART: A Structured Dataset of Regulatory Drug Documents in Italian for Clinical NLP
• Authors: Mariano Barone, Antonio Laudante, Giuseppe Riccio, Antonio Romano, Marco Postiglione, Vincenzo Moscato,
• Abstract summary: DART is a structured corpus of Italian summaries of product characteristics from the Italian Medicines Agency (AIFA)<n>It provides structured information on key pharmacological domains such as indications, adverse drug reactions, and drug-drug interactions.<n>To validate its utility, we implemented an LLM-based drug interaction checker that leverages the dataset to infer clinically meaningful interactions.
• Score: 10.905164788230913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The extraction of pharmacological knowledge from regulatory documents has become a key focus in biomedical natural language processing, with applications ranging from adverse event monitoring to AI-assisted clinical decision support. However, research in this field has predominantly relied on English-language corpora such as DrugBank, leaving a significant gap in resources tailored to other healthcare systems. To address this limitation, we introduce DART (Drug Annotation from Regulatory Texts), the first structured corpus of Italian Summaries of Product Characteristics derived from the official repository of the Italian Medicines Agency (AIFA). The dataset was built through a reproducible pipeline encompassing web-scale document retrieval, semantic segmentation of regulatory sections, and clinical summarization using a few-shot-tuned large language model with low-temperature decoding. DART provides structured information on key pharmacological domains such as indications, adverse drug reactions, and drug-drug interactions. To validate its utility, we implemented an LLM-based drug interaction checker that leverages the dataset to infer clinically meaningful interactions. Experimental results show that instruction-tuned LLMs can accurately infer potential interactions and their clinical implications when grounded in the structured textual fields of DART. We publicly release our code on GitHub: https://github.com/PRAISELab-PicusLab/DART.

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