This paper describes the development of an AI-driven decision-making support system designed to assist health care professionals in managingpharmacologicalprescriptionsanddruginteractions, withafocusoncardiovascular diseases. The system utilizes Tiny Language Models (TLMs) to improve patient safety, treatment effectiveness, and reduce medication errors. The main objective is to symbiotically support the healthcare professionals by leveraging the artificial intelligence system’s ability to rapidly and precisely retrieve and extract information.

SICURA: A Symbiotic AI System to Support Pharmacological Management

Raffaele Manna;Giulia Speranza;Maria Pia di Buono;
2025-01-01

Abstract

This paper describes the development of an AI-driven decision-making support system designed to assist health care professionals in managingpharmacologicalprescriptionsanddruginteractions, withafocusoncardiovascular diseases. The system utilizes Tiny Language Models (TLMs) to improve patient safety, treatment effectiveness, and reduce medication errors. The main objective is to symbiotically support the healthcare professionals by leveraging the artificial intelligence system’s ability to rapidly and precisely retrieve and extract information.
2025
Inglese
Luca Manzoni Luca Bortolussi Giulia Cisotto Fabio Anselmi
Joint Proceedings of the Thematic Workshops at Ital-IA 2025 colocated with the 5th National Conference on Artificial Intelligence, organized by CINI (Ital-IA 2025)
Ital-IA 2025: 5th National Conference on Artificial Intelligence - Thematic Workshops
1
5
5
https://ceur-ws.org/Vol-4121/Ital-IA_2025_paper_62.pdf
June 23-24, 2025
Trieste (Italy)
Internazionale
no
5
Manna, Raffaele; Speranza, Giulia; Di Buono, Maria Pia; Troia, Gianmarco; Vizzini, Giovanni
open
273
info:eu-repo/semantics/conferenceObject
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11574/246820
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