Health AI Central
Peer-Reviewed Publication
Comput Biol Med2025;198(Pt A):111135.November 1, 2025Journal Article

Enhanced LLM-supported instructions for medication use through retrieval-augmented generation.

Davi Dos Reis de Jesus1, Antônio Pereira de Souza Júnior1, Elisa Tuler de Albergaria1, Adriana Silvina Pagano2, Isaias Jose Ramos de Oliveira3, Cristiane Dos Santos Dias3, Eura Martins Lage3, Flavia Ribeiro de Oliveira3, Juliana Almeida Oliveira3, Igor de Carvalho Gomes4, Leonardo Chaves Dutra da Rocha1, Zilma Silveira Nogueira Reis5
1Department of Computer Science, Universidade Federal de São João Del Rei, Praça Frei Orlando, 170, Centro, São João del-Rei, Minas Gerais, 36307-352, Brazil.
2Arts Faculty, Universidade Federal de Minas Gerais, Avenida Presidente Antônio Carlos, 6627 - Pampulha, Belo Horizonte, MG, 31270-901, Brazil.
3Faculty of Medicine, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 190, Belo Horizonte, 30130-100, Brazil.
4National Secretary of Primary Care of the Brazilian Ministry of Health, Brasília. Esplanada dos Ministérios, Bloco G 1.o andar, Brasília, DF, 70058-900, Brazil.
5Faculty of Medicine, Universidade Federal de Minas Gerais, Avenida Professor Alfredo Balena, 190, Belo Horizonte, 30130-100, Brazil. Electronic address: zilma.medicina@gmail.com.
PubMed DOI

Abstract

Prescription systems support medication treatment by standardizing content, enhancing legibility and promoting correct dispensation. Challenges remain to personalize use instructions with sufficient clarity to meet patients' needs. Large Language Models (LLMs) are promising to improve communication in healthcare. This study investigates LLM Retrieval-Augmented Generation (RAG) drawing on patient i…

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