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Related Experiment Video

Updated: Jun 13, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Language-Specific Differences in Large Language Model Diagnostic Reasoning: A Translation-Controlled Clinical

Jakub Magdziarz Ibrahim-El-Nur¹, Wojciech Kaczmarek², Weronika Winiarska¹

¹Department of Social Medicine and Public Health, Medical University of Warsaw, Pawińskiego 3a, 02-106 Warsaw, Poland.

Journal of Clinical Medicine

|June 12, 2026

Summary

Large language models (LLMs) show varied performance across languages in clinical diagnostics. Multilingual LLM evaluation is crucial before deploying them in non-English healthcare systems to ensure safety and efficacy.

Keywords:

clinical decision support systems differential diagnosis large language models natural language processing

Related Experiment Videos

Last Updated: Jun 13, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Area of Science:

Artificial Intelligence in Medicine
Natural Language Processing
Clinical Decision Support

Background:

Large language models (LLMs) are increasingly assessed for clinical diagnostic tasks.
LLM performance may differ significantly across languages, impacting their global applicability.
Evaluating language influence is vital for safe deployment in diverse healthcare settings.

Purpose of the Study:

To investigate if input language affects LLM diagnostic reasoning in clinical vignettes.
To provide insights for multilingual predeployment evaluations of LLMs in non-English healthcare systems.

Main Methods:

A translation-controlled in silico study using 30 clinical vignettes in English and Polish.
Six LLMs were evaluated using a structured reflection framework with physician raters.
Analysis of 720 rater-level evaluations and 360 model-language-vignette responses.

Main Results:

Language significantly impacted LLM performance, varying by model; some models performed better in English.
Differences were most pronounced in differential diagnosis quality and examination planning, not just final diagnosis.
Language robustness was not consistent across evaluated LLMs, with performance gaps in safety-relevant reasoning domains.

Conclusions:

Multilingual clinical performance of LLMs is highly model-dependent.
Language-specific evaluations are essential before deploying LLMs in non-English healthcare systems.
Performance disparities highlight the need for tailored validation to ensure clinical safety and effectiveness.