Position: Open and Closed Large Language Models in Healthcare

Abstract

This position paper provides an analysis of 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. Closed LLMs, such as GPT-4, have dominated high-performance applications, particularly in medical imaging and multimodal diagnostics, due to their advanced reasoning capabilities. 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.

Cite

Text

Xu et al. "Position: Open and Closed Large Language Models in Healthcare." NeurIPS 2024 Workshops: GenAI4Health, 2024.

Markdown

[Xu et al. "Position: Open and Closed Large Language Models in Healthcare." NeurIPS 2024 Workshops: GenAI4Health, 2024.](https://mlanthology.org/neuripsw/2024/xu2024neuripsw-position/)

BibTeX

@inproceedings{xu2024neuripsw-position,
  title     = {{Position: Open and Closed Large Language Models in Healthcare}},
  author    = {Xu, Jiawei and Ding, Ying and Bu, Yi},
  booktitle = {NeurIPS 2024 Workshops: GenAI4Health},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/xu2024neuripsw-position/}
}