Large Language Models: A Guide for Radiologists

被引:22
|
作者
Kim, Sunkyu [1 ,2 ]
Lee, Choong-kun [3 ]
Kim, Seung-seob [4 ,5 ,6 ]
机构
[1] Korea Univ, Dept Comp Sci & Engn, Seoul, South Korea
[2] AIGEN Sci, Seoul, South Korea
[3] Yonsei Univ, Dept Internal Med, Div Med Oncol, Coll Med, Seoul, South Korea
[4] Yonsei Univ Coll Med, Severance Hosp, Res Inst Radiol Sci, Dept Radiol, Seoul, South Korea
[5] Yonsei Univ, Severance Hosp, Dept Radiol, Coll Med, 50-1 Yonsei Ro, Seoul 03722, South Korea
[6] Yonsei Univ, Severance Hosp, Res Inst Radiol Sci, Coll Med, 50-1 Yonsei Ro, Seoul 03722, South Korea
关键词
Natural language processing; Large language model; Transformer; Radiology; Chatbot; ChatGPT;
D O I
10.3348/kjr.2023.0997
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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.
引用
收藏
页码:126 / 133
页数:8
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