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

In: Economic Resilience and Sustainability—Vol. 1

Author

Listed:
  • 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)

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.

Suggested Citation

  • Carlos Luengo Vera & Antonio Javier De Lucas López & Victor Ramos Arroyo & M. Teresa de Val Núñez, 2025. "Optimizing AI in Medical Education: Cost-Benefit Analysis of Large Language Models in the MIR Examination," Springer Proceedings in Business and Economics, in: Veland Ramadani & Abdylmenaf Bexheti & Hyrije Abazi-Alili & Christina Theodoraki & Gadaf Rexhepi & B (ed.), Economic Resilience and Sustainability—Vol. 1, chapter 0, pages 451-473, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-04218-7_28
    DOI: 10.1007/978-3-032-04218-7_28
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