Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages
arXiv:2605.30529v1 Announce Type: cross Abstract: Sentence-embedding models for semantic search are overwhelmingly developed and evaluated on English corpora. When applied to clinical retrieval in other languages -- particularly retrieval of ICD-10-CM / CIE-10 codes -- recall degrades in ways often masked by aggregate benchmarks. We study whether large generative language models can serve as data factories to close this gap. We build a two-stage retriever (bi-encoder followed by cross-encoder reranker), fine-tuned from a Spanish biomedical encoder (PlanTL-GOB-ES/bsc-bio-ehr-es) on Gemini-gener
The proliferation of Large Generative Language Models (LLMs) and increasing demand for localized AI applications beyond English are driving researchers to address data limitations for other languages.
This study advances the capability of AI in critical sectors like healthcare for non-English speaking populations, potentially improving diagnostic accuracy and operational efficiency on a global scale.
The explicit focus on using LLMs as data factories to improve non-English semantic search for clinical coding changes how language barriers in specialized AI applications are being actively addressed.
- · Non-English healthcare systems
- · AI model developers specializing in localization
- · Patients in non-English speaking regions
- · General-purpose, English-centric AI models
- · Legacy manual coding processes
Improved accuracy in clinical coding and semantic search for non-English medical data.
Accelerated development and adoption of AI-powered diagnostic and administrative tools in diverse linguistic contexts.
Global health equity could improve as advanced AI benefits extend beyond English-speaking healthcare systems.
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Read at arXiv cs.LG