Large language models in neurology research and future practice
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Published version
Date
2023-12-04
Authors
Romano, Michael F.
Shih, Ludy C.
Paschalidis, Ioannis C.
Au, Rhoda
Kolachalama, Vijaya B.
Version
Published version
OA Version
Citation
M.F. Romano, L.C. Shih, I.C. Paschalidis, R. Au, V.B. Kolachalama. 2023. "Large Language Models in Neurology Research and Future Practice." Neurology, Volume 101, Issue 23, pp.1058-1067. https://doi.org/10.1212/WNL.0000000000207967
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.
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License
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.