Large Language Models in Neurology Research and Future Practice

被引:17
|
作者
Romano, Michael F. [1 ,2 ]
Shih, Ludy C. [3 ]
Paschalidis, Ioannis C. [4 ,5 ,6 ]
Au, Rhoda [1 ,3 ,7 ,8 ,9 ]
Kolachalama, Vijaya B. [1 ,5 ,6 ,10 ]
机构
[1] Boston Univ, Dept Med, Chobanian & Avedisian Sch Med, Boston, MA 02118, Brazil
[2] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA USA
[3] Boston Univ, Dept Neurol, Chobanian & Avedisian Sch Med, Boston, MA USA
[4] Boston Univ, Dept Elect & Comp Engn, Div Syst Engn, Boston, MA USA
[5] Boston Univ, Dept Biomed Engn, Boston, MA USA
[6] Boston Univ, Fac Comp & Data Sci, Boston, MA 02215 USA
[7] Boston Univ, Chobanian & Avedisian Sch Med, Dept Anat & Neurobiol, Framingham Heart Study, Boston, MA USA
[8] Boston Univ, Dept Epidemiol, Sch Publ Hlth, Boston, MA USA
[9] Boston Univ, Alzheimers Dis Res Ctr, Boston, MA USA
[10] Boston Univ, Dept Comp Sci, Boston, MA 02215 USA
基金
美国国家科学基金会;
关键词
CLASSIFICATION; GPT-4;
D O I
10.1212/WNL.0000000000207967
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders.
引用
收藏
页码:1058 / 1067
页数:10
相关论文
共 50 条
  • [21] When Protein Structure Embedding Meets Large Language Models
    Ali, Sarwan
    Chourasia, Prakash
    Patterson, Murray
    GENES, 2024, 15 (01)
  • [22] Distilling large language models for matching patients to clinical trials
    Nievas, Mauro
    Basu, Aditya
    Wang, Yanshan
    Singh, Hrituraj
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2024, 31 (09) : 1953 - 1963
  • [23] Rethinking Legal Compliance Automation: Opportunities with Large Language Models
    Hassani, Shabnam
    Sabetzadeh, Mehrdad
    Amyot, Daniel
    Liao, Jain
    32ND IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE, RE 2024, 2024, : 432 - 440
  • [24] Comparing Code Explanations Created by Students and Large Language Models
    Leinonen, Juho
    Denny, Paul
    MacNeil, Stephen
    Sarsa, Sami
    Bernstein, Seth
    Kim, Joanne
    Tran, Andrew
    Hellas, Arto
    PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL 1, 2023, : 124 - 130
  • [25] Efficacy of large language models and their potential in Obstetrics and Gynecology education
    Eoh, Kyung Jin
    Kwon, Gu Yeun
    Lee, Eun Jin
    Lee, Joonho
    Lee, Inha
    Kim, Young Tae
    Nam, Eun Ji
    OBSTETRICS & GYNECOLOGY SCIENCE, 2024, 67 (06) : 550 - 556
  • [26] Assessing the Code Clone Detection Capability of Large Language Models
    Zhang, Zixian
    Saber, Takfarinas
    PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON CODE QUALITY, ICCQ 2024, 2024,
  • [27] Methodologies for Email Spam Classification using Large Language Models
    De La Noval, Alejandro
    Gutierrez, Diana
    Soni, Jayesh
    Upadhyay, Himanshu
    Perez-Pons, Alexander
    Lagos, Leonel
    2023 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE, CSCI 2023, 2023, : 179 - 185
  • [28] Large Language Models to Help Appeal Denied Radiotherapy Services
    Kiser, Kendall J.
    Waters, Michael
    Reckford, Jocelyn
    Lundeberg, Christopher
    Abraham, Christopher D.
    JCO CLINICAL CANCER INFORMATICS, 2024, 8
  • [29] Solving Proof Block Problems Using Large Language Models
    Poulsen, Seth
    Sarsa, Sami
    Prather, James
    Leinonen, Juho
    Becker, Brett A.
    Hellas, Arto
    Denny, Paul
    Reeves, Brent N.
    PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1, 2024, : 1063 - 1069
  • [30] Zero-Shot Classification of Art With Large Language Models
    Tojima, Tatsuya
    Yoshida, Mitsuo
    IEEE ACCESS, 2025, 13 : 17426 - 17439