Open-Source Large Language Models and AI Health Equity: A Health Service Triangle Model Perspective.

Area of Science:

• Health Informatics
• Artificial Intelligence
• Health Equity

Background:

• Artificial intelligence (AI) is increasingly used in healthcare, but disparities in access and application create health inequalities.
• The health service triangle model provides a framework for analyzing healthcare delivery and identifying areas for improvement.

Purpose of the Study:

• To explore the role of open-source large language models (LLMs) in promoting AI health equity.
• To assess the current landscape of AI health inequalities.
• To demonstrate how open-source LLMs can democratize AI-enabled health services.

Main Methods:

• Defining AI health and categorizing AI-supported decision-making patterns.
• Comparing open-source and closed-source LLMs based on patient privacy, data security, accessibility, and usability.
• Applying the health service triangle model to analyze the impact of open-source LLMs on health services.

Main Results:

• Open-source LLMs demonstrate distinct advantages over closed-source models for AI-enabled health services.
• Open-source LLMs can expand service types, improve accessibility, enhance quality, and reduce costs in healthcare.
• These models show particular benefit for low-resource regions, driving democratization of AI health services.

Conclusions:

• Open-source LLMs are pivotal in enabling AI health equity through technological sharing.
• Addressing challenges like hallucination risks and ethical considerations is crucial for the responsible deployment of open-source LLMs.
• Open-source LLMs have the potential to significantly reduce health disparities and improve global health outcomes.