ArticleEuropean journal of investigation in health, psychology and education2026
Performance of Large Language Models on Cognitive Aptitude Testing: A Multi-Run Evaluation on the German Medical School Admission Test (TMS).
Article in European journal of investigation in health, psychology and education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND AND
objectivesLarge language models (LLMs) have demonstrated high performance on knowledge-based medical examinations but their capabilities on cognitive aptitude tests emphasizing reasoning and abstraction remain underexplored. The Test for Medical Studies (TMS), a German medical school admission test, provides a standardized framework to examine these capabilities. This study aimed to evaluate the performance and consistency of multiple LLMs on text-based and visual-analytic TMS items. MATERIALS AND
methodsEight contemporary LLMs, comprising proprietary and open-source systems, were evaluated using a multi-run design on standardized TMS items spanning text-based and visual-analytic cognitive domains.
resultsMean accuracy remained substantially below levels typically reported for knowledge-based medical examinations, with marked performance differences between text-based and visual-analytic subtests. Open-source models performed competitively compared with proprietary systems. Inter-run reliability was heterogeneous, indicating notable variability across repeated evaluations.
conclusionsCurrent LLMs show limited and domain-dependent performance on cognitive aptitude tasks relevant to medical school admission. High accuracy on knowledge-based examinations does not translate into stable performance on aptitude tests emphasizing fluid intelligence. The observed modality-dependent performance patterns and inter-run variability highlight the importance of differentiated, multi-run evaluation strategies when assessing LLMs for applications in medical education.
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