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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods

Livia Maria Strasser¹, Wilma Anschuetz², Fabio Dennstädt^1,3

¹Medical Knowledge and Decision Support, School of Medicine, University of St.Gallen, St.Jakobstrasse 21, St.Gallen, 9000, Switzerland, +41 71 224 32 00.

JMIR Medical Education

|March 11, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) show varied accuracy on medical questions across languages, with German performing best. Prompting in English generally improved results, but human oversight is crucial for reliable integration into medical education.

Keywords:

LLM LLM evaluation education large language model medical question answering multiple-choice questions natural language processing

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Area of Science:

Medical Education
Artificial Intelligence
Natural Language Processing

Background:

Artificial intelligence (AI) is transforming healthcare and medical education.
Large language models (LLMs) demonstrate potential in medical licensing exams.
LLM performance varies by language, necessitating cross-language comparisons.

Purpose of the Study:

Evaluate LLM performance on medical multiple-choice questions across German, French, and Italian.
Quantitatively and qualitatively assess LLM capabilities in multilingual medical education.
Identify factors influencing LLM accuracy in diverse linguistic contexts.

Main Methods:

Mixed methods study analyzing 114 multiple-choice questions in German, French, and Italian.
Quantitative performance analysis of multiple LLMs (OpenAI, Meta AI, Anthropic, DeepSeek).
Qualitative analysis of answer explanations from top-performing LLMs (GPT4o, Claude-Sonnet-3.7) for incorrect answers.

Main Results:

LLM accuracy varied significantly by model and language (64%-87%), with German questions yielding the best performance.
English prompts generally outperformed language-matched prompts, though top models showed comparable results.
Qualitative analysis revealed reasoning errors in LLM explanations and identified 3 imprecise questions.

Conclusions:

LLM performance in medical exams is influenced by model, prompt, and input language, requiring careful selection.
LLM-generated explanations can enhance medical question quality, contingent on data security.
Human oversight is essential for nuanced medical content, and ongoing evaluation is needed for reliable LLM integration.