Lost in Translation: Cross-Cultural Bias in LLM-Assisted Medical Symptom Interpretation

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Devices & Health Technology · Depth: Advanced, medium

Summary

A pilot study by Tian et al. investigates cross-cultural bias in Large Language Model (LLM)-assisted medical symptom interpretation, specifically focusing on how GPT-5 mini converts patient language into clinical summaries. The research operationalizes patient symptom descriptions into four styles: direct English, indirect English, culturally mediated English, and Chinese-original language. Using a compact red-teaming framework, the study evaluated GPT-5 mini across eight symptom scenarios, each expressed in these four styles, generating 32 vignettes. After repeating the evaluation three times with generic and culture-aware prompts, 192 model outputs were produced. While GPT-5 mini generally preserved broad symptom categories, subtle failure modes emerged. Culture-aware prompting reduced severity downgrades from 14.6% to 9.4% and ambiguity-flagging failures from 28.1% to 13.5%, though interpretation inconsistency and clinical category shift remained at 6.2%. Indirect English showed the highest severity-downgrade and flagging-failure rates, while Chinese-original expressions were often correctly categorized but not consistently flagged as ambiguous.

Key takeaway

For AI Scientists and NLP Engineers developing medical LLMs, you must integrate cultural robustness into your evaluation frameworks. Your models, like GPT-5 mini, can exhibit subtle biases in interpreting patient symptoms across diverse linguistic and cultural expressions. Prioritize assessing severity framing, ambiguity preservation, and the need for human review escalation. Implement culture-aware prompting to reduce severity downgrades and ambiguity-flagging failures, ensuring more reliable and equitable clinical summaries.

Key insights

LLM medical symptom interpretation exhibits cross-cultural biases, necessitating culture-aware evaluation and prompting.

Principles

• Patient language variation impacts LLM interpretation.
• Culture-aware prompts mitigate specific LLM biases.
• Ambiguity flagging is crucial for medical LLMs.

Method

A red-teaming framework tests LLM interpretation across four linguistic/cultural styles using generic and culture-aware prompts.

In practice

• Implement culture-aware prompting for medical LLMs.
• Evaluate LLMs for severity framing and ambiguity preservation.
• Incorporate human-review escalation for ambiguous cases.

Topics

• LLM Bias
• Cross-Cultural Communication
• Medical NLP
• Symptom Interpretation
• Red Teaming
• GPT-5 mini

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.