Large language models in health care: Development, applications, and challenges

Author:

Yang Rui1,Tan Ting Fang2,Lu Wei3,Thirunavukarasu Arun James4ORCID,Ting Daniel Shu Wei25,Liu Nan56ORCID

Affiliation:

1. Department of Biomedical Informatics, Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore

2. Singapore National Eye Center, Singapore Eye Research Institute Singapore Health Service Singapore Singapore

3. StatNLP Research Group Singapore University of Technology and Design Singapore

4. University of Cambridge School of Clinical Medicine Cambridge UK

5. Duke‐NUS Medical School Centre for Quantitative Medicine Singapore Singapore

6. Duke‐NUS Medical School Programme in Health Services and Systems Research Singapore Singapore

Abstract

AbstractRecently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre‐training. In the last 3 years, these domain‐specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs.

Publisher

Wiley

Reference64 articles.

1. Attention is all you need;Vaswani A;Adv Neural Inf Process Syst,2017

2. DevlinJ ChangM‐W LeeK ToutanovaK. BERT: pre‐training of deep bidirectional transformers for language understanding.2018.https://doi.org/10.48550/arXiv.1810.04805

3. PaLM: scaling language modeling with pathways;Chowdhery A;arXiv:2204.02311,2022

4. TouvronH LavrilT IzacardG MartinetX LachauxM‐A LacroixT et al. LLaMA: open and efficient foundation language models. 2023.http://arxiv.org/abs/2302.13971

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3