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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.09906 |
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| _version_ | 1866910787918888960 |
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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 |