arXiv:2606.24200v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) in clinical settings increasingly requires multilingual retrieval against predominantly English evidence corpora. Multilingual medical retrieval demands three capabilities: cross-lingual alignment, concept discrimination, and evidence retrieval. However, existing benchmarks evaluate these only in isolation, leaving the interaction between biomedical expertise and multilingual coverage unmeasured. We introduce MMed-Bench-IR, a benchmark designed to disentangle these axes across 6 languages and three structura
The increasing adoption of RAG models in clinical settings and the global nature of medical knowledge necessitate better multilingual retrieval capabilities, which this benchmark addresses.
Improved multilingual medical information retrieval can democratize access to critical health data and enhance AI agent performance in diverse linguistic environments.
The introduction of MMed-Bench-IR provides a standardized, heterogeneous benchmark for evaluating multilingual medical RAG systems, allowing for more robust development and comparison.
- · AI developers
- · Healthcare providers
- · Non-English speaking populations
- · Medical research
- · Monolingual AI solutions
- · Healthcare systems with language barriers
More accurate and reliable multilingual medical AI applications will emerge.
Reduced healthcare disparities due to improved access to medical knowledge across languages.
Acceleration of medical research and drug discovery through more comprehensive global data leverage.
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Read at arXiv cs.AI