## 1 Introduction  
Large Language Models (LLMs), a class of artificial intelligence (AI) systems designed for complex language processing tasks, are increasingly being applied in healthcare and medical education [1, 2]. These models can retrieve domain-specific knowledge, generate clinical reasoning pathways, and simulate decision-making processes, which position them as promising tools for diagnostic assistance and medical training. Their performance on English-language assessments, such as the United States Medical Licensing Examination (USMLE), has approached or exceeded that of human examinees in recent studies [3, 4].  
Despite these advances, there is a limited understanding of how LLMs perform in high-stakes, non-English medical certification settings. This gap is especially relevant given the linguistic, cultural, and curricular variability in the global medical education systems. Evaluating model performance in such contexts is essential to determine whether LLMs generalize beyond English-dominant datasets and standardized examinations.  
The Chilean National Certification Examination in Anesthesiology (CONACAM), a rigorous Spanish-language board exam, provides an ideal setting for benchmarking AI performance in a specialized clinical field. Anesthesiology requires not only factual knowledge of pharmacology, physiology, and critical care but also situational decision-making and clinical reasoning. These multifaceted cognitive requirements are likely to challenge current LLMs, which tend to excel in recall-based tasks but may falter on items that require inference, synthesis, or contextual nuance.  
While previous studies evaluating AI on multiple-choice questions have primarily relied on Classical Test Theory (CTT) [5, 6], which captures surface-level accuracy, this study integrates Item Response Theory (IRT)--specifically the one-parameter logistic Rasch model--to provide deeper insight into latent ability and item difficulty. To further enhance interpretability, we classified all test items according to [PERSON]'s revised taxonomy [7, 8] (Recall, Understand, Apply, Analyze), which enabled a stratified analysis across cognitive domains.  
In addition, we conducted a qualitative error analysis of incorrect LLM responses using a classification framework adapted from [PERSON] et al. (2024) [9]. Each justification was reviewed and coded into one of four error types: reasoning-based, knowledge-based, reading comprehension error, or plausible non-error. This allowed us to probe the underlying mechanisms of failure, moving beyond simple correctness to assess the cognitive and interpretive limitations of LLMs in clinical decision-making.  
We hypothesized that closed-source models (e.g., GPT-4o and GPT-o1) would outperform their open-source counterparts (e.g., LLaMA 3 and Deepseek-R1) due to their extensive training datasets and architectural refinements. Nonetheless, we anticipated that all models would underperform on high-difficulty and high-discrimination items, especially those requiring deep reasoning. Human examinees are expected to maintain an advantage in tasks involving contextual judgment and clinical nuances.  
The primary aim of this study was to benchmark the performance of nine state-of-the-art LLMs against human anesthesiology candidates using psychometrically calibrated exam data. By incorporating cognitive complexity analysis and error taxonomy, we offer a multidimensional evaluation framework that enhances the understanding of LLM behavior in educational and clinical assessment contexts.