Version 1
: Received: 10 July 2024 / Approved: 10 July 2024 / Online: 11 July 2024 (10:02:27 CEST)
How to cite:
Amugongo, L. M.; Mascheroni, P.; Brooks, S. G.; Doering, S.; Seidel, J. Retrieval Augmented Generation for Large Language Models in Healthcare: A Systematic Review. Preprints2024, 2024070876. https://doi.org/10.20944/preprints202407.0876.v1
Amugongo, L. M.; Mascheroni, P.; Brooks, S. G.; Doering, S.; Seidel, J. Retrieval Augmented Generation for Large Language Models in Healthcare: A Systematic Review. Preprints 2024, 2024070876. https://doi.org/10.20944/preprints202407.0876.v1
Amugongo, L. M.; Mascheroni, P.; Brooks, S. G.; Doering, S.; Seidel, J. Retrieval Augmented Generation for Large Language Models in Healthcare: A Systematic Review. Preprints2024, 2024070876. https://doi.org/10.20944/preprints202407.0876.v1
APA Style
Amugongo, L. M., Mascheroni, P., Brooks, S. G., Doering, S., & Seidel, J. (2024). Retrieval Augmented Generation for Large Language Models in Healthcare: A Systematic Review. Preprints. https://doi.org/10.20944/preprints202407.0876.v1
Chicago/Turabian Style
Amugongo, L. M., Stefan Doering and Jan Seidel. 2024 "Retrieval Augmented Generation for Large Language Models in Healthcare: A Systematic Review" Preprints. https://doi.org/10.20944/preprints202407.0876.v1
Abstract
Large Language Models (LLMs) have demonstrated promising capabilities to solve complex tasks in critical sectors such as healthcare. However, LLMs are limited by their training data which is often outdated, the tendency to generate inaccurate ("hallucinated") content and a lack of transparency in the content they generate. To address these limitations, retrieval augmented generation (RAG) grounds the responses of LLMs by exposing them to external knowledge sources. However, in the healthcare domain there is currently a lack of systematic understanding of which datasets, RAG methodologies and evaluation frameworks are available. This review aims to bridge this gap by assessing RAG-based approaches employed by LLMs in healthcare, focusing on the different steps of retrieval, augmentation and generation. Additionally, we identify the limitations, strengths and gaps in the existing literature. Our synthesis shows that proprietary models such as GPT-3.5/4 are the most used for RAG applications in healthcare. Also, there is a lack of standardized evaluation frameworks for RAG-based applications. In addition, the majority of the studies do not assess or address ethical considerations related to RAG in healthcare. It is important to account for ethical challenges that are inherent when AI systems are implemented in the clinical setting. Lastly, we highlight the need for further research and development to ensure responsible and effective adoption of RAG in the medical domain.
Keywords
Retrieval Augmented Generation; Large Language Models; Healthcare; Evaluation; AI ethics
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
