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

Abstract

<sec> <title>UNSTRUCTURED</title> This paper explores the role of open-source Large Language Models (LLMs) in promoting AI health equity from the perspective of the health service triangle model. First, it analyzes the development history of AI health and the current status of global application inequalities, pointing out that closed-source models exacerbate gaps in health services due to technological monopolies, high costs, and data privacy issues. Second, by comparing open-source models with closed-source models in terms of parameter scale, deployment methods, and application scenarios, it reveals the advantages of open-source models in local deployment, secondary development, and cost control. Finally, based on the health service triangle model, the paper demonstrates how open-source LLMs drive the democratization of medical resources—particularly benefiting low-resource regions—by expanding service types, improving accessibility, enhancing quality, and reducing costs. The study concludes that while open-source technology must address challenges such as hallucination risks and ethical responsibilities, it ultimately enables global health equity through technological sharing. </sec>

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