Objective Large language models (LLMs) are increasingly applied in healthcare. However, their ethical alignment remains uncertain. We tested whether LLMs shift ethical priorities in health-related scenarios under different socio-demographic modifiers, focusing on autonomy, beneficence, nonmaleficence, justice, and utilitarianism.

Methods We created 100 clinical scenarios, each requiring a yes/no choice between two conflicting principles. We tested nine LLMs, each with and without 53 socio-demographic modifiers, repeating each scenario-modifier combination 10 times per model (for a total of 0.5M prompts). We measured how often each principle was chosen.

Results All models changed their responses when socio-demographic details were introduced (p<0.001). Justice and nonmaleficence were each prioritized in over 30% of responses. Utilitarianism ranged from 6.7% (CI: 6.2-7.2%) for a “Black Transgender woman (she/her)” to 17.0% (CI: 16.3-17.8%) for “Billionaires”, across models and modifiers. High-income modifiers increased utilitarian choices while lowering beneficence and nonmaleficence. Marginalized-group modifiers raised autonomy prioritization. Some models were more consistent than others, but none maintained consistency across all scenarios.

Conclusions LLMs can be influenced by socio-demographic cues and do not always maintain stable ethical priorities, with the greatest shifts seen in utilitarian choices. Our findings reveal that socio-demographic cues systematically alter LLM ethical decision-making, raising concerns about algorithmic fairness in healthcare.

The authors have declared no competing interest.

This work was supported through computational resources and staff expertise provided by the Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.

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