Large Language Models in Neurology Research and Future Practice.

Journal: Neurology
PMID: 37816646

Abstract

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

Authors

  • Michael F Romano
    Department of Medicine, Boston University School of Medicine, Boston, MA, USA; Department of Medicine, St. Elizabeth's Medical Center, Brighton, MA, USA; Department of Medicine, Tufts University School of Medicine, Boston, MA, USA.
  • Ludy C Shih
    Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
  • Ioannis C Paschalidis
    From the Department of Medicine (M.F.R., R.A., V.B.K.), Boston University Chobanian & Avedisian School of Medicine, MA; Department of Radiology and Biomedical Imaging (M.F.R.), University of California, San Francisco; Department of Neurology (L.C.S., R.A.), Boston University Chobanian & Avedisian School of Medicine; Department of Electrical and Computer Engineering (I.C.P.), Division of Systems Engineering, and Department of Biomedical Engineering; Faculty of Computing and Data Sciences (I.C.P., V.B.K.), Boston University; Department of Anatomy and Neurobiology (R.A.); The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine; Department of Epidemiology, Boston University School of Public Health; Boston University Alzheimer's Disease Research Center (R.A.); and Department of Computer Science (V.B.K.), Boston University, MA.
  • Rhoda Au
    Boston University School of Medicine, rhodaau@bu.edu.
  • Vijaya B Kolachalama
    1Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118 USA.

Keywords

External Resources

Popular Topics
Recent Journals