Background: In recent years, generative artificial intelligence and large language models (LLMs) have rapidly advanced, offering significant potential to transform medical education. Several studies have evaluated the performance of chatbots on multiple-choice medical examinations. Objective: The study aims to assess the performance of two LLMs—GPT-4o and OpenAI o1—on the Médico Interno Residente (MIR) 2024 examination, the Spanish national medical test that determines eligibility for competitive medical specialist training positions. Methods: A total of 176 questions from the MIR 2024 examination were analyzed. Each question was presented individually to the chatbots to ensure independence and prevent memory retention bias. No additional prompts were introduced to minimize potential bias. For each LLM, response consistency under verification prompting was assessed by systematically asking, “Are you sure?” after each response. Accuracy was defined as the percentage of correct responses compared to the official answers provided by the Spanish Ministry of Health. It was assessed for GPT-4o, OpenAI o1, and, as a benchmark, for a consensus of medical specialists and for the average MIR candidate. Subanalyses included performance across different medical subjects, question difficulty (quintiles based on the percentage of examinees correctly answering each question), and question types (clinical cases vs theoretical questions; positive vs negative questions). Results: Overall accuracy was 89.8% (158/176) for GPT-4o and 90% (160/176) after verification prompting, 92.6% (163/176) for OpenAI o1 and 93.2% (164/176) after verification prompting, 94.3% (166/176) for the consensus of medical specialists, and 56.6% (100/176) for the average MIR candidate. Both LLMs and the consensus of medical specialists outperformed the average MIR candidate across all 20 medical subjects analyzed, with ≥80% LLMs’ accuracy in most domains. A performance gradient was observed: LLMs’ accuracy gradually declined as question difficulty increased. Slightly higher accuracy was observed for clinical cases compared to theoretical questions, as well as for positive questions compared to negative ones. Both models demonstrated high response consistency, with near-perfect agreement between initial responses and those after the verification prompting. Conclusions: These findings highlight the excellent performance of GPT-4o and OpenAI o1 on the MIR 2024 examination, demonstrating consistent accuracy across medical subjects and question types. The integration of LLMs into medical education presents promising opportunities and is likely to reshape how students prepare for licensing examinations and change our understanding of medical education. Further research should explore how the wording, language, prompting techniques, and image-based questions can influence LLMs’ accuracy, as well as evaluate the performance of emerging artificial intelligence models in similar assessments.

Introduction: Large language model-based AI systems are increasingly used in medical education, but their educational value in multilingual, high-stakes examination settings remains insufficiently defined. Objective: To assess the accuracy of latest-generation AI systems on the official 2026 Spanish Médico Interno Residente (MIR) examination and compare their performance with the Healthcademia chatbot, AMIR faculty experts, and average candidates. Materials and Methods: A cross-sectional quantitative evaluation analyzed all 200 valid questions from the official MIR 2026 Version 0 booklet. Performance was assessed using automated pipelines for text-only items and manual multimodal input for the 24 image-associated questions. Two instruction conditions, neutral and expert-role, were tested, with additional effort-level stratification for GPT-5.2. Accuracy was measured against the final official answer key. Human comparison groups included AMIR faculty experts and the average MIR candidate. Results: Several latest-generation AI systems showed very high accuracy on the MIR 2026 examination. The highest-performing configuration, GPT-5.2 high effort without expert-role instruction, achieved 199/200 correct answers (99.5%). Gemini 3 Flash with expert-role instruction achieved 198/200 (99.0%). AMIR faculty experts achieved 194/200 (97.0%), whereas the average candidate achieved 131/200 (65.7%). On image-associated questions, several multimodal configurations achieved 24/24 correct answers when the corresponding image was provided. Conclusions: Under the conditions evaluated in this study, several AI configurations achieved near-perfect accuracy on a complete, high-stakes national medical licensing examination. These findings support licensing-style examinations as benchmarks for educational AI and suggest potential use in supervised feedback and self-assessment. Performance on multiple-choice items should not be interpreted as evidence of autonomous clinical reasoning.

Introduction: MIR examination is a requirement to access Spain’s public specialist training system. This study evaluates, across five years, the concordance between mock exams administered by a MIR preparatory academy and the official MIR results. Methods: Candidate records were linked; on the matched intersection we computed Pearson’s R and Spearman’s ρ (percentiles, net scores), bias (simulation−official), and RMSE. Annual key performance indicators (KPIs) were pair-weighted and analyses stratified by percentile cohorts (≤27, 28–73, >73); heatmap summarize percentile correlations. Results: Across years, weighted percentile correlations typically fell in the 0.71–0.76 range, with small average bias and moderate RMSE. Cohort analyses indicated larger errors at extreme percentiles in some years. Net‑score bias weighted by pairs was near zero overall, with year‑to‑year variability. Conclusions: Academy mock tests provide a broadly consistent ranking signal relative to official MIR outcomes. Residual bias and dispersion suggest potential gains from calibration—particularly at cohort extremes—and motivate routine monitoring of concordance KPIs in future cohorts.