Analysis evaluates trade-offs between closed and open large language models in clinical systems, highlighting the need for balanced collaboration to protect patient privacy and control costs.
Integrating Large Language Models (LLMs) into healthcare promises substantial advancements but requires careful consideration of technical, ethical, and regulatory challenges. Closed LLMs of private companies offer ease of deployment but pose risks related to data privacy and vendor dependence. Open LLMs deployed on local hardware enable greater model customization but demand resources and technical expertise. Balancing these approaches, with collaboration among clinicians, researchers, and companies is crucial to ensure effective, secure, and ethical implementation.
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Dennstädt et al. (2025) studied this question.
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