From experimentation to adoption: a normative ethical analysis of large language models in health care

Abstract

<title>Abstract</title> Background Large language models (LLMs) are increasingly embedded in routine health-care communication, extending beyond experimental evaluation to real-world use by clinicians, patients, and caregivers. This transition from experimentation to adoption represents a normatively consequential ethical shift. While early research has focused primarily on technical performance, emerging ethical challenges arise less from model capabilities than from how LLMs become integrated into clinical workflows, institutional arrangements, and relationships of care. Methods We conducted a normative ethical analysis informed by empirical evidence from recent real-world studies of LLM adoption in health-care settings. Drawing on adoption-oriented empirical research, we examined how the transition from experimental use to routine reliance reshapes ethical conditions related to trust, responsibility allocation, and equity across different user groups. Results The analysis identifies a systematic shift in ethical risk from model-level concerns—such as accuracy, validation, and bias mitigation—toward system-level dynamics that emerge during routine adoption. Adoption alters how medical information is mediated, redistributes responsibility among clinicians, institutions, and patients, and exposes asymmetrical vulnerability linked to digital literacy, educational background, and regional context. Rather than uniformly democratizing access to medical knowledge, routine LLM use may function as a capability amplifier, magnifying existing inequities in the absence of institutional safeguards. Conclusions Ethical challenges arising from routine LLM adoption in health care cannot be adequately addressed through model-level or pre-deployment ethics alone. Addressing these challenges requires adoption-sensitive governance approaches that recognize reliance as an ongoing ethical process. Institutional accountability, role-sensitive design, and continuous oversight are ethically necessary to ensure that LLM integration enhances health-care communication and access without eroding trust or exacerbating existing disparities.

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