The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI
Takeshi Nakaura, Rintaro Ito, Daiju Ueda, Taiki Nozaki, Yasutaka Fushimi, Yusuke Matsui, Masahiro Yanagawa, Akira Yamada, Takahiro Tsuboyama, Noriyuki Fujima, Fuminari Tatsugami, Kenji Hirata, Shohei Fujita, Koji Kamagata, Tomoyuki Fujioka, Mariko Kawamura, Shinji Naganawa
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.