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Main Authors: Xu, Jiawei, Ding, Ying, Bu, Yi
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2501.09906
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author Xu, Jiawei
Ding, Ying
Bu, Yi
author_facet Xu, Jiawei
Ding, Ying
Bu, Yi
contents This position paper analyzes the evolving roles of open-source and closed-source large language models (LLMs) in healthcare, emphasizing their distinct contributions and the scientific community's response to their development. Due to their advanced reasoning capabilities, closed LLMs, such as GPT-4, have dominated high-performance applications, particularly in medical imaging and multimodal diagnostics. Conversely, open LLMs, like Meta's LLaMA, have gained popularity for their adaptability and cost-effectiveness, enabling researchers to fine-tune models for specific domains, such as mental health and patient communication.
format Preprint
id arxiv_https___arxiv_org_abs_2501_09906
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Position: Open and Closed Large Language Models in Healthcare
Xu, Jiawei
Ding, Ying
Bu, Yi
Computers and Society
This position paper analyzes the evolving roles of open-source and closed-source large language models (LLMs) in healthcare, emphasizing their distinct contributions and the scientific community's response to their development. Due to their advanced reasoning capabilities, closed LLMs, such as GPT-4, have dominated high-performance applications, particularly in medical imaging and multimodal diagnostics. Conversely, open LLMs, like Meta's LLaMA, have gained popularity for their adaptability and cost-effectiveness, enabling researchers to fine-tune models for specific domains, such as mental health and patient communication.
title Position: Open and Closed Large Language Models in Healthcare
topic Computers and Society
url https://arxiv.org/abs/2501.09906