Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea.
AIGEN Sciences, Seoul, Republic of Korea.
Korean J Radiol. 2024 Feb;25(2):126-133. doi: 10.3348/kjr.2023.0997.
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.
大型语言模型(LLMs)已经彻底改变了全球技术格局,其应用已经超越了自然语言处理领域。由于在大型数据集上进行了广泛的预训练,当代的 LLM 可以处理各种任务,从通用功能到特定领域,如放射学,而无需额外的微调。基于 LLM 的通用聊天机器人可以优化放射科医生在其专业工作和研究方面的效率。重要的是,这些 LLM 正在快速发展,其中包括解决“幻觉”、高培训成本和效率问题,以及纳入多模态输入等方面。在这篇综述中,我们旨在通过简洁的概述和对特定于放射学方面的总结,为有兴趣利用 LLM 的放射科医生提供概念知识和可操作的指导,从现在开始到潜在的未来方向。