The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.
Jpn J Radiol
; 42(7): 685-696, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38551772
ABSTRACT
The advent of Deep Learning (DL) has significantly propelled the field of diagnostic radiology forward by enhancing image analysis and interpretation. The introduction of the Transformer architecture, followed by the development of Large Language Models (LLMs), has further revolutionized this domain. LLMs now possess the potential to automate and refine the radiology workflow, extending from report generation to assistance in diagnostics and patient care. The integration of multimodal technology with LLMs could potentially leapfrog these applications to unprecedented levels.However, LLMs come with unresolved challenges such as information hallucinations and biases, which can affect clinical reliability. Despite these issues, the legislative and guideline frameworks have yet to catch up with technological advancements. Radiologists must acquire a thorough understanding of these technologies to leverage LLMs' potential to the fullest while maintaining medical safety and ethics. This review aims to aid in that endeavor.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Radiologia
/
Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
Jpn J Radiol
Assunto da revista:
DIAGNOSTICO POR IMAGEM
/
RADIOLOGIA
/
RADIOTERAPIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Japão