Optimizing AI in Medical Education: Cost-Benefit Analysis of Large Language Models in the MIR Examination

Carlos Luengo Vera (), Antonio Javier De Lucas López (), Victor Ramos Arroyo () and M. Teresa de Val Núñez ()
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Carlos Luengo Vera: Universidad de Alcalá, Faculty of Business and Economics
Antonio Javier De Lucas López: Universidad de Alcalá, Faculty of Business and Economics
Victor Ramos Arroyo: Universidad de Alcalá, Faculty of Business and Economics
M. Teresa de Val Núñez: Universidad de Alcalá, Faculty of Business and Economics

Chapter Chapter 28 in Economic Resilience and Sustainability—Vol. 1, 2025, pp 451-473 from Springer

Abstract: Abstract This study explores the cost-effectiveness and sustainability of large language models (LLMs) in medical education, focusing on their application in Spain’s MIR examination. It evaluates trade-offs between accuracy, computational cost, and practical feasibility. Findings reveal that Miri Pro, a domain-specific model, surpassed generalist LLMs in both accuracy (195/210) and cost-efficiency, outperforming the best human score. High-end models like GPT-4 Turbo demonstrated advanced reasoning but incurred high costs per correct response. The study highlights the need for cost-optimized, fine-tuned AI solutions and questions the scalability of premium models. It introduces a novel cost-benefit framework, advancing debate on AI’s viability in resource-constrained settings. Practical implications include prioritizing AI literacy, regulatory equity, and financially sustainable AI integration in medical education.

Keywords: Large language models; Medical education; Cost-benefit analysis; AI in healthcare; MIR examination; AI-assisted learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-04218-7_28

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DOI: 10.1007/978-3-032-04218-7_28

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