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MINI REVIEW article

Front. Med.
Sec. Regulatory Science
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1477898
This article is part of the Research Topic Large Language Models for Medical Applications View all 4 articles

Large Language Models in Patient Education: A Scoping Review of Applications in Medicine

Provisionally accepted
Serhat Aydin Serhat Aydin 1Mert Karabacak Mert Karabacak 2*Victoria Vlachos Victoria Vlachos 3Konstantinos Margetis Konstantinos Margetis 2*
  • 1 Koç University, Istanbul, Istanbul, Türkiye
  • 2 Mount Sinai Health System, New York, United States
  • 3 College of Human Ecology, Cornell University, Ithaca, New York, United States

The final, formatted version of the article will be published soon.

    Introduction: Large Language Models (LLMs) are sophisticated algorithms that analyze and generate vast amounts of textual data, mimicking human communication. Notable LLMs include GPT-4o by Open AI, Claude 3.5 Sonnet by Anthropic, and Gemini by Google. This scoping review aims to synthesize the current applications and potential uses of LLMs in patient education and engagement.Materials and Methods: Following the PRISMA-ScR checklist and methodologies by Arksey, O'Malley, and Levac, we conducted a scoping review. We searched PubMed in June 2024, using keywords and MeSH terms related to LLMs and patient education. Two authors conducted the initial screening, and discrepancies were resolved by consensus. We employed thematic analysis to address our primary research question.The review identified 201 studies, predominantly from the United States (58.2%). Six themes emerged: generating patient education materials, interpreting medical information, providing lifestyle recommendations, supporting customized medication use, offering perioperative care instructions, and optimizing doctor-patient interaction. LLMs were found to provide accurate responses to patient queries, enhance existing educational materials, and translate medical information into patient-friendly language. However, challenges such as readability, accuracy, and potential biases were noted.Discussion: LLMs demonstrate significant potential in patient education and engagement by creating accessible educational materials, interpreting complex medical information, and enhancing communication between patients and healthcare providers. Nonetheless, issues related to the accuracy and readability of LLM-generated content, as well as ethical concerns, require further research and development. Future studies should focus on improving LLMs and ensuring content reliability while addressing ethical considerations.

    Keywords: Large Language Models1, ChatGPT2, patient education3, Artificial Intelligence4, machine learning5, deep learning5 For instance, is…emains limited. A notable…owever, a significant apps

    Received: 08 Aug 2024; Accepted: 03 Oct 2024.

    Copyright: © 2024 Aydin, Karabacak, Vlachos and Margetis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Mert Karabacak, Mount Sinai Health System, New York, United States
    Konstantinos Margetis, Mount Sinai Health System, New York, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.