Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning

2026-01-13

AI Summary

This study addresses the significant language gap in multilingual medical reasoning, where low-resource languages exhibit substantially weaker performance compared to English, hindering equitable deployment of medical AI. To bridge this gap, the authors propose a language-aware collaborative reasoning framework that simultaneously generates reasoning paths in both English and the local language, abstracts them into structured conceptual representations, and fuses local clinical knowledge with the English logical scaffold at the conceptual level. This approach introduces, for the first time, a language collaboration mechanism leveraging concept-level alignment, structured knowledge integration, and model distillation to markedly enhance both medical reasoning accuracy and cultural appropriateness for low-resource languages. Evaluated on three multilingual medical benchmarks, the method achieves an average performance gain of 5%, with expert assessments confirming its clinical validity and cultural alignment.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 design combines the structural robustness of English reasoning with the practice-grounded expertise encoded in local languages. To evaluate multilingual medical reasoning beyond multiple-choice settings, we construct MultiMed-X, a benchmark covering seven languages with expert-annotated long-form question answering and natural language inference tasks, comprising 350 instances per language. Experiments across three benchmarks show that Med-CoReasoner improves multilingual reasoning performance by an average of 5%, with particularly substantial gains in low-resource languages. Moreover, model distillation and expert evaluation analysis further confirm that Med-CoReasoner produces clinically sound and culturally grounded reasoning traces.

Problem

• multilingual gap
• medical reasoning
• language disparities
• global medical deployment
• local languages

Innovation

• language-informed co-reasoning
• multilingual medical reasoning
• concept-level alignment
• clinical knowledge integration
• MultiMed-X benchmark