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Ethical Risks and Structural Implications of AI-Mediated Medical Interpreting.

Alexandra Lopez Vera¹

¹California University of Science and Medicine, 1501 Violet St, Colton, CA, 92324, United States, 1 9095809661.

|February 5, 2026

Summary

Artificial intelligence (AI) tools for medical interpreting pose risks to accuracy and equity, especially for low-resource languages. AI should supplement, not replace, human interpreters to ensure patient safety and equitable care.

Keywords:

AI-mediated interpreting artificial intelligence clinical communication health equity language access

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Area of Science:

Medical Ethics
Public Health Communication
Health Equity

Background:

Artificial intelligence (AI) is increasingly adopted for medical interpreting and public health communication.
Current AI translation systems present significant risks regarding accuracy, confidentiality, and equity, particularly for speakers of low-resource languages.
Limitations include struggles with regional dialects, figurative language, cultural nuances, and sensitive health topics, potentially causing critical misunderstandings.

Purpose of the Study:

To examine the ethical and structural implications of AI-mediated interpreting in clinical and public health settings.
To argue against the routine use of AI as a replacement for professional medical interpreters.
To propose that AI tools should serve as supervised, supplementary resources.

Main Methods:

This viewpoint analyzes the ethical considerations and structural impacts of AI in healthcare language access.
It reviews the limitations of current AI translation models in diverse linguistic and cultural contexts.
The analysis considers data privacy, surveillance, and equity concerns associated with AI systems.

Main Results:

Routine use of AI as a substitute for qualified interpreters risks normalizing a lower standard of care for non-English speakers.
AI systems can exacerbate existing health disparities due to inherent limitations and data privacy issues.
Over-reliance on AI threatens patient safety, informed consent, and trust in healthcare systems.

Conclusions:

AI tools in medical interpreting should be optional, carefully evaluated supplements, not replacements for human interpreters.
Clear regulatory frameworks for transparency, accountability, and community oversight are essential for AI implementation.
Ensuring equitable language access requires prioritizing human expertise, language rights, and structural commitments to equity over automated solutions.