MedExpQA: A Multilingual Benchmark for Evaluating LLMs on Medical Question Answering (AI in Medicine, Sep 2024)

Forum topic · 小凯 · 2026-07-05

Summary

MedExpQA is a multilingual benchmark introduced in a September 2024 paper published in Artificial Intelligence in Medicine for evaluating large language models (LLMs) on medical question answering across multiple languages. The benchmark is built from medical expertise questions, enabling standardized comparison of how well LLMs perform in specialized medical knowledge domains beyond English. Work of this kind addresses a known gap: most medical QA evaluations are English-centric, while real clinical practice worldwide operates in many languages. MedExpQA provides a reproducible evaluation protocol so that researchers and practitioners can measure cross-lingual generalization, identify weaknesses in low-resource medical languages, and track progress as models evolve. The paper appears in the Elsevier journal Artificial Intelligence in Medicine and is accessible via ScienceDirect. This zhichai.net entry indexes the paper under vertical-domain AI applications, alongside related work on cross-lingual evaluation of LLMs. Readers should consult the original PDF for quantitative results, dataset statistics, and model leaderboards, as this listing summarizes only the publicly available metadata and title information.

MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering

Overview

MedExpQA: Multilingual benchmarking of Large Language Models for Medical Question Answering is a research paper published in September 2024 in the journal *Artificial Intelligence in Medicine* (Elsevier).

Source: ScienceDirect article page
Venue: Artificial Intelligence in Medicine, September 2024
Topic: Vertical-domain AI — medical question answering with LLMs

What the paper addresses

The paper introduces a multilingual benchmark for medical question answering, motivated by the observation that most evaluations of large language models on medical knowledge are English-centric. Since clinical practice and medical education take place in many languages worldwide, multilingual evaluation is essential to understand whether LLMs generalize their medical competence across languages.

Significance for the community

Provides a standardized, reproducible protocol for cross-lingual medical QA evaluation.
Enables comparison of LLM performance in medical languages beyond English, highlighting gaps in low-resource settings.
Complements related cross-lingual evaluation efforts (e.g., "Better to Ask in English?" studies) indexed in this collection.

Notes

The listing above is based on the paper's public metadata. For quantitative results, benchmark statistics, and model rankings, please refer to the original article via the ScienceDirect link.

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