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
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