Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications
Issued Date
2024-03-01
Resource Type
ISSN
1010660X
eISSN
16489144
Scopus ID
2-s2.0-85188954082
Journal Title
Medicina (Lithuania)
Volume
60
Issue
3
Rights Holder(s)
SCOPUS
Bibliographic Citation
Medicina (Lithuania) Vol.60 No.3 (2024)
Suggested Citation
Miao J., Thongprayoon C., Suppadungsuk S., Garcia Valencia O.A., Cheungpasitporn W. Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications. Medicina (Lithuania) Vol.60 No.3 (2024). doi:10.3390/medicina60030445 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/97866
Title
Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications
Author's Affiliation
Corresponding Author(s)
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Abstract
The integration of large language models (LLMs) into healthcare, particularly in nephrology, represents a significant advancement in applying advanced technology to patient care, medical research, and education. These advanced models have progressed from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data, thus improving medical practice efficiency and effectiveness. A significant challenge in medical applications of LLMs is their imperfect accuracy and/or tendency to produce hallucinations—outputs that are factually incorrect or irrelevant. This issue is particularly critical in healthcare, where precision is essential, as inaccuracies can undermine the reliability of these models in crucial decision-making processes. To overcome these challenges, various strategies have been developed. One such strategy is prompt engineering, like the chain-of-thought approach, which directs LLMs towards more accurate responses by breaking down the problem into intermediate steps or reasoning sequences. Another one is the retrieval-augmented generation (RAG) strategy, which helps address hallucinations by integrating external data, enhancing output accuracy and relevance. Hence, RAG is favored for tasks requiring up-to-date, comprehensive information, such as in clinical decision making or educational applications. In this article, we showcase the creation of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease. This example demonstrates its potential in providing specialized, accurate medical advice, marking a step towards more reliable and efficient nephrology practices.