Ethical implications of using general-purpose LLMs in clinical settings: a comparative analysis of prompt engineering strategies and their impact on patient safety

Abstract

Current clinical applications of general-purpose LLMs present substantial ethical challenges requiring urgent attention. While structured prompt engineering demonstrated measurable improvements in some domains, with meta-cognitive approaches showing 13.0% performance gains and safety-first prompting reducing critical incidents by 45%, substantial limitations persist across all strategies. Even optimized approaches achieved inadequate performance in communication and empathy (≤ 54% of maximum), retained residual bias patterns (11.7% in safety-first conditions), and exhibited concerning safety deficits, indicating that current prompt engineering methods provide only marginal improvements, which are insufficient for reliable clinical deployment. These findings highlight significant ethical challenges that necessitate further investigation into the development of appropriate guidelines and regulatory frameworks for the clinical use of general-purpose AI models.

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