Performance of single-agent and multi-agent language models in Spanish language medical competency exams

Abstract

BACKGROUND: Large language models (LLMs) like GPT-4o have shown promise in advancing medical decision-making and education. However, their performance in Spanish-language medical contexts remains underexplored. This study evaluates the effectiveness of single-agent and multi-agent strategies in answering questions from the EUNACOM, a standardized medical licensure exam in Chile, across 21 medical specialties. METHODS: GPT-4o was tested on 1,062 multiple-choice questions from publicly available EUNACOM preparation materials. Single-agent strategies included Zero-Shot, Few-Shot, Chain-of-Thought (CoT), Self-Reflection, and MED-PROMPT, while multi-agent strategies involved Voting, Weighted Voting, Borda Count, MEDAGENTS, and MDAGENTS. Each strategy was tested under three temperature settings (0.3, 0.6, 1.2). Performance was assessed by accuracy, and statistical analyses, including Kruskal-Wallis and Mann-Whitney U tests, were performed. Computational resource utilization, such as API calls and execution time, was also analyzed. RESULTS: MDAGENTS achieved the highest accuracy with a mean score of 89.97% (SD = 0.56%), outperforming all other strategies (p < 0.001). MEDAGENTS followed with a mean score of 87.99% (SD = 0.49%), and the CoT with Few-Shot strategy scored 87.67% (SD = 0.12%). Temperature settings did not significantly affect performance (F2,54 = 1.45, p = 0.24). Specialty-level analysis showed the highest accuracies in Psychiatry (95.51%), Neurology (95.49%), and Surgery (95.38%), while lower accuracies were observed in Neonatology (77.54%), Otolaryngology (76.64%), and Urology/Nephrology (76.59%). Notably, several exam questions were correctly answered using simpler single-agent strategies without employing complex reasoning or collaboration frameworks. CONCLUSIONS AND RELEVANCE: Multi-agent strategies, particularly MDAGENTS, significantly enhance GPT-4o's performance on Spanish-language medical exams, leveraging collaboration to improve diagnostic accuracy. However, simpler single-agent strategies are sufficient to address many questions, high-lighting that only a fraction of standardized medical exams require sophisticated reasoning or multi-agent interaction. These findings suggest potential for LLMs as efficient and scalable tools in Spanish-speaking healthcare, though computational optimization remains a key area for future research.

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