arXiv:2606.07853v1 Announce Type: cross Abstract: Large Language Models are transforming the support for clinical decision and their application in real scenarios. Yet, most benchmarks are conducted in English, and cross-lingual evaluation is needed to tackle the language gaps in global access. We introduce ClinicalBr, the first bilingual benchmark for clinical decision built from real Brazilian case reports. The corpus contains 2,892 cases drawn from 28 SciELO medical journals, spanning 18 specialties, and is structured as parallel Portuguese-English pairs. Each case supports four evaluation
The proliferation of Large Language Models (LLMs) globally necessitates rigorous evaluation beyond English, particularly as their application in critical fields like clinical decision-making expands.
This development highlights the critical need for locally relevant and culturally appropriate AI models, suggesting a future where AI's utility is tied to its multilingual and multi-cultural applicability, potentially fragmenting the global AI market.
The explicit recognition and development of non-English benchmarks for LLMs in critical domains like healthcare indicate a shift towards more localized AI development and deployment, moving away from an English-centric default.
- · Non-English speaking countries
- · Local AI development firms
- · Healthcare providers in emerging markets
- · Multilingual AI research
- · English-only LLM developers
- · Monolingual AI evaluation frameworks
Increased investment in creating localized datasets and models for various languages and cultural contexts.
Greater adoption of AI in healthcare in non-English speaking regions due to improved relevancy and trust.
The emergence of 'AI sovereignty' in data and models as nations prioritize local language support for critical applications.
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Read at arXiv cs.AI