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

被引:88
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
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 96 条
[1]   Potential Use Cases for ChatGPT in Radiology Reporting [J].
Abou Elkassem, Asser ;
Smith, Andrew D. .
AMERICAN JOURNAL OF ROENTGENOLOGY, 2023, 221 (03) :373-376
[2]  
Alsentzer E., 2019, ARXIV
[3]  
[Anonymous], PROMPT ENG LILLOG
[4]   Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum [J].
Ayers, John W. ;
Poliak, Adam ;
Dredze, Mark ;
Leas, Eric C. ;
Zhu, Zechariah ;
Kelley, Jessica B. ;
Faix, Dennis J. ;
Goodman, Aaron M. ;
Longhurst, Christopher A. ;
Hogarth, Michael ;
Smith, Davey M. .
JAMA INTERNAL MEDICINE, 2023, 183 (06) :589-596
[5]   On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? [J].
Bender, Emily M. ;
Gebru, Timnit ;
McMillan-Major, Angelina ;
Shmitchell, Shmargaret .
PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, 2021, :610-623
[6]  
Bengio Y, 2001, ADV NEUR IN, V13, P932
[7]   GPT-4 in Radiology: Improvements in Advanced Reasoning [J].
Bhayana, Rajesh ;
Bleakney, Robert R. ;
Krishna, Satheesh .
RADIOLOGY, 2023, 307 (05)
[8]   Performance of ChatGPT on a Radiology Board-style Examination: Insights into Current Strengths and Limitations [J].
Bhayana, Rajesh ;
Krishna, Satheesh ;
Bleakney, Robert R. .
RADIOLOGY, 2023, 307 (05)
[9]   PACS: the silent revolution [J].
Bick, U ;
Lenzen, H .
EUROPEAN RADIOLOGY, 1999, 9 (06) :1152-1160
[10]  
Bills S., 2023, OPENAI