Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions

被引:60
|
作者
D'Antonoli, Tugba Akinci [1 ]
Stanzione, Arnaldo [2 ]
Bluethgen, Christian [3 ]
Vernuccio, Federica [4 ]
Ugga, Lorenzo [2 ]
Klontzas, Michail E. [5 ,6 ,7 ]
Cuocolo, Renato [8 ]
Cannella, Roberto [9 ]
Kocak, Burak [10 ]
机构
[1] Cantonal Hosp Baselland, Inst Radiol & Nucl Med, Liestal, Switzerland
[2] Univ Naples Federico II, Dept Adv Biomed Sci, Naples, Italy
[3] Univ Zurich, Univ Hosp Zurich, Inst Diagnost & Intervent Radiol, Zurich, Switzerland
[4] Univ Hosp Padova, Dept Radiol, Padua, Italy
[5] Univ Hosp Heraklion, Dept Med Imaging, Iraklion, Greece
[6] Univ Crete, Dept Radiol, Iraklion, Greece
[7] FORTH, Inst Comp Sci, Computat Biomed Lab, Iraklion, Greece
[8] Univ Salerno, Dept Med Surg & Dent, Baronissi, Italy
[9] Univ Palermo, Sect Neurosurg, Dept Biomed Neurosci & Adv Diagnost, Palermo, Italy
[10] Univ Hlth Sci, Basaksehir Cam & Sakura City Hosp, Clin Radiol, Istanbul, Turkiye
来源
DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY | 2024年 / 30卷 / 02期
关键词
Large language models; natural language processing; artificial intelligence; ChatGPT; deep learning;
D O I
10.4274/dir.2023.232417
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
With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions.
引用
收藏
页码:80 / 90
页数:11
相关论文
共 50 条
  • [21] Foundation and large language models: fundamentals, challenges, opportunities, and social impacts
    Devon Myers
    Rami Mohawesh
    Venkata Ishwarya Chellaboina
    Anantha Lakshmi Sathvik
    Praveen Venkatesh
    Yi-Hui Ho
    Hanna Henshaw
    Muna Alhawawreh
    David Berdik
    Yaser Jararweh
    Cluster Computing, 2024, 27 : 1 - 26
  • [22] Large Language Models and the Future of Organization Theory
    Cornelissen, Joep
    Hollerer, Markus A.
    Boxenbaum, Eva
    Faraj, Samer
    Gehman, Joel
    ORGANIZATION THEORY, 2024, 5 (01):
  • [23] Large language models: a primer and gastroenterology applications
    Shahab, Omer
    El Kurdi, Bara
    Shaukat, Aasma
    Nadkarni, Girish
    Soroush, Ali
    THERAPEUTIC ADVANCES IN GASTROENTEROLOGY, 2024, 17
  • [24] Ethical and Theological Challenges of Large Language Models
    Strahornik, Vojko
    BOGOSLOVNI VESTNIK-THEOLOGICAL QUARTERLY-EPHEMERIDES THEOLOGICAE, 2023, 83 (04): : 839 - 852
  • [25] Large language models in patient education: a scoping review of applications in medicine
    Aydin, Serhat
    Karabacak, Mert
    Vlachos, Victoria
    Margetis, Konstantinos
    FRONTIERS IN MEDICINE, 2024, 11
  • [26] Based on Medicine, The Now and Future of Large Language Models
    Su, Ziqing
    Tang, Guozhang
    Huang, Rui
    Qiao, Yang
    Zhang, Zheng
    Dai, Xingliang
    CELLULAR AND MOLECULAR BIOENGINEERING, 2024, 17 (04) : 263 - 277
  • [27] Flying Into the Future With Large Language Models
    Kanjilal, Sanjat
    CLINICAL INFECTIOUS DISEASES, 2024, 78 (04) : 867 - 869
  • [28] Multimodal Large Language Models in Health Care:Applications,Challenges, and Future Outlook
    AlSaad, Rawan
    Abd-alrazaq, Alaa
    Boughorbel, Sabri
    Ahmed, Arfan
    Renault, Max-Antoine
    Damseh, Rafat
    Sheikh, Javaid
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2024, 26
  • [29] The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI
    Nakaura, Takeshi
    Ito, Rintaro
    Ueda, Daiju
    Nozaki, Taiki
    Fushimi, Yasutaka
    Matsui, Yusuke
    Yanagawa, Masahiro
    Yamada, Akira
    Tsuboyama, Takahiro
    Fujima, Noriyuki
    Tatsugami, Fuminari
    Hirata, Kenji
    Fujita, Shohei
    Kamagata, Koji
    Fujioka, Tomoyuki
    Kawamura, Mariko
    Naganawa, Shinji
    JAPANESE JOURNAL OF RADIOLOGY, 2024, 42 (07) : 685 - 696
  • [30] Multi-modal large language models in radiology: principles, applications, and potential
    Shen, Yiqiu
    Xu, Yanqi
    Ma, Jiajian
    Rui, Wushuang
    Zhao, Chen
    Heacock, Laura
    Huang, Chenchan
    ABDOMINAL RADIOLOGY, 2024, : 2745 - 2757