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Related Experiment Video

Updated: May 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large Language Model-Generated Patient Instructions for Prescriptions in Primary Health Care: Preclinical Algorithm

Zilma Silveira Nogueira Reis1, Elisa Tuler Albergaria2, Adriana Silvina Pagano3

  • 1Health Informatics Center, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.

Journal of Medical Internet Research
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Generative artificial intelligence (AI) can simplify medication instructions, improving patient adherence. Open-source AI models show promise, though human oversight is crucial for safe integration into electronic prescribing.

Keywords:
digital healthdrug prescriptionsgenerative artificial intelligencelarge language modelsprimary health care

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Natural Language Processing for Medical Information

Background:

  • Generative AI offers potential to simplify medication instructions, enhancing patient health and treatment adherence.
  • Improving clarity of medication use instructions is key for effective primary healthcare.

Purpose of the Study:

  • Evaluate the performance of large language models (LLMs) in generating medication usage instructions.
  • Compare ChatGPT-4.0, Llama3.1-8B, and Llama3.1-8B-RAG for generating prescription-complementary instructions.

Main Methods:

  • Randomized, blinded experimental preclinical study involving 62 healthcare professionals.
  • LLMs (ChatGPT-4.0, Llama3.1-8B, Llama3.1-8B-RAG) generated instructions based on patient information leaflets.
  • Assessed instructions on adequacy, completeness, clarity, language simplification, usefulness, and errors.

Main Results:

  • All models produced qualified instructions, with ChatGPT-4.0 scoring highest overall (median 88.4).
  • Llama3.1-8B-RAG performed comparably to ChatGPT-4.0 in adequacy, completeness, clarity, and usefulness.
  • Error and hallucination frequencies were similar across models; ChatGPT-4.0 excelled in language simplification.

Conclusions:

  • Open-source LLMs (like Llama3.1-8B-RAG) show comparable performance to closed-source models (ChatGPT-4.0), except in language simplification.
  • LLM-generated instructions have potential but require prescriber validation and governance for safe electronic prescribing integration.