Mar 10, 2025

AI Hallucinations in Healthcare: Cross-Cultural and Linguistic Risks of LLMs in Low-Resource Languages

Peace Silly, Zineb Ibnou Cheikh, Gracia Kaglan

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Summary

This project explores AI hallucinations in healthcare across cross-cultural and linguistic contexts, focusing on English, French, Arabic, and a low-resource language, Ewe. We analyse how large language models like GPT-4, Claude, and Gemini generate and disseminate inaccurate health information, emphasising the challenges faced by low-resource languages.

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@misc {

title={

AI Hallucinations in Healthcare: Cross-Cultural and Linguistic Risks of LLMs in Low-Resource Languages

author={

Peace Silly, Zineb Ibnou Cheikh, Gracia Kaglan

date={

3/10/25

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Reviewer Name

Constructive critique

Criteria 1:

Advancing interpretability (Possible questions to answer in this criteria: Does the project contribute to the field of mechanistic interpretability? Does it provide new insights into understanding or steering AI model behavior? How well does it move us towards reprogramming AI models? How original and innovative is the approach?)

2.5

Criteria 2:

Relevance for AI safety (Possible questions to answer in this criteria: How important is the contribution to advancing the field of AI safety? Do we expect the results to generalize beyond the specific case(s) presented in the submission? Does the approach introduce new safety mechanisms or enhance existing ones in innovative ways?))

2.5

Criteria 3:

Methodology & presentation (Possible questions to answer in this criteria: How well is the project executed from a technical standpoint?, Is the code well-structured, documented, and reproducible?, How effectively does it utilize Goodfire's SDK/API and other provided resources?, How clearly and effectively is the research presented in the paper?, Quality of visualizations and demos (if applicable), Clarity of methodology explanation and results interpretation))

2.5

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