Large Language Models: A Guide for Radiologists
- Author:
Sunkyu KIM
1
;
Choong-kun LEE
;
Seung-seob KIM
Author Information
- Publication Type:Focus
- From:Korean Journal of Radiology 2024;25(2):126-133
- CountryRepublic of Korea
- Language:English
- 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 LLMs are on a trajectory of rapid evolution, 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.
