Large Language Models: A Guide for Radiologists

74Citations
Citations of this article
56Readers
Mendeley users who have this article in their library.

Abstract

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these ution, wherein challenges such as “hallucination,” high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.

Cite

CITATION STYLE

APA

Kim, S., Lee, C. K., & Kim, S. S. (2024). Large Language Models: A Guide for Radiologists. Korean Journal of Radiology, 25(2), 126–133. https://doi.org/10.3348/kjr.2023.0997

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free