Tailored Strategies for Applying Large Language Models in Clinical Settings and Addressing Data Security Challenges

Abstract

Since the advent of ChatGPT in 2022, large language models (LLMs) have rapidly evolved, and their clinical applications are currently being explored. This paper introduces three practical strategies for applying LLMs in healthcare settings: text-to-text, any-to-text, and retrieval-augmented generation. Each strategy is described using real-world examples and analyzed for potential data security risks. Although LLMs offer promising efficiency and performance benefits, they also pose new challenges regarding privacy and information leakage, particularly when trained using sensitive patient data. We propose tailored learning and governance approaches to mitigate such risks, emphasizing the necessity of de-identification techniques and robust guardrails for ensuring safe and effective deployment in clinical settings.

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