Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning
arXiv:2601.08267v3 Announce Type: replace Abstract: While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially weaker reasoning in local languages, limiting equitable global medical deployment. To bridge this gap, we introduce Med-CoReasoner, a language-informed co-reasoning framework that elicits parallel English and local-language reasoning, abstracts them into structured concepts, and integrates local clinical knowledge into an English logical scaffold via concept-level alignment and retrieval. This des
The proliferation of powerful large language models necessitates addressing their limitations for equitable global application, especially in critical sectors like medicine. The paper proposes a concrete, novel solution for multilingual medical AI.
This development addresses a critical gap in the equitable global deployment of AI, particularly in medicine, by enabling more effective reasoning in local languages, which can broaden access to AI-powered diagnostics and treatment.
The ability of large language models to perform complex medical reasoning accurately will no longer be as heavily skewed towards English, potentially leading to more localized and culturally relevant AI healthcare solutions.
- · Multilingual medical AI developers
- · Non-English speaking healthcare providers
- · Patients in diverse linguistic regions
- · Global health initiatives
- · Companies whose advantage relied solely on English-centric AI datasets
- · Monolingual AI research paradigms
Improved accuracy and accessibility of medical AI in non-English speaking regions.
Increased investment in multilingual medical data collection and localized AI model training.
Potential for new medical research insights derived from diverse linguistic and cultural clinical data, previously untapped by English-only models.
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