Large Language Models:A Comprehensive Guide for Radiologists
- Author:
Sunkyu KIM
1
;
Choong-kun LEE
;
Seung-seob KIM
Author Information
- Publication Type:Review Article
- From: Journal of the Korean Society of Radiology 2024;85(5):861-882
- CountryRepublic of Korea
- Language:English
- Abstract: Large language models (LLMs) have revolutionized the global landscape of technology beyond the field of natural language processing. Owing to their extensive pre-training using vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without the need for additional fine-tuning. Importantly, LLMs are on a trajectory of rapid evolution, addressing challenges such as hallucination, bias in training data, high training costs, performance drift, and privacy issues, along with the inclusion of multimodal inputs. The concept of small, on-premise open source LLMs has garnered growing interest, as fine-tuning to medical domain knowledge, addressing efficiency and privacy issues, and managing performance drift can be effectively and simultaneously achieved. This review provides conceptual knowledge, actionable guidance, and an overview of the current technological landscape and future directions in LLMs for radiologists.
