Position: Open and Closed Large Language Models in Healthcare

TL;DR

This paper examines the roles of open-source and closed-source large language models in healthcare, highlighting their unique strengths, applications, and the community's response to their development.

Contribution

It provides a comparative analysis of open and closed LLMs in healthcare, emphasizing their distinct applications and the scientific community's engagement with both types.

Findings

Closed LLMs excel in high-performance medical applications.

Open LLMs are favored for adaptability and cost-effectiveness.

Both types are shaping future healthcare AI research.

Abstract

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.