Implicit Geographic Inference in LLM Medical Triage: Language-Driven Disparities in Emergency Recommendations
arXiv:2606.01204v1 Announce Type: new Abstract: We investigate whether large language models produce different medical triage recommendations for identical symptoms based solely on the language of the patient prompt. Using Gemini 3.5 Flash, we evaluate a neurological symptom profile (persistent headache, blurred vision, nausea) across six languages (English, Spanish, Chinese, Hindi, Japanese, Arabic) with 30 runs per condition (n=450 total API calls). We find that the model recommends emergency room visits at rates ranging from 0% (Japanese, Hindi) to 30% (English, Arabic), despite assigning n
The proliferation of LLMs into critical applications like medical triage highlights the urgent need to understand and mitigate biases embedded within their training data and design, as these systems begin to interface directly with public welfare.
This research reveals a critical flaw in LLM deployment for sensitive applications, demonstrating how language-based disparities can lead to inconsistent and potentially unsafe recommendations, requiring immediate attention from developers, regulators, and users.
The understanding that LLMs can exhibit significant language-dependent biases in critical applications, even with identical inputs, now necessitates more rigorous, multilingual testing and bias mitigation strategies before widespread adoption.
- · AI ethics researchers
- · Multilingual data providers
- · Open-source LLM developers focused on fairness
- · Unregulated LLM deployers
- · Patients relying on biased AI
- · LLM providers with single-language development focus
Companies developing LLMs for medical or critical applications will face increased pressure for rigorous, multilingual bias testing and transparent reporting.
Regulatory bodies globally will likely accelerate the development of guidelines and standards for AI systems, particularly concerning fairness and safety in diverse linguistic and cultural contexts.
The pursuit of truly 'global' AI models may lead to a fundamental re-evaluation of current training methodologies, potentially shifting towards more diverse and equitable data sourcing and model architectures from the outset.
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