Entre la eficiencia académica y el razonamiento clínico: perfiles funcionales de uso de inteligencia artificial en estudiantes de medicina

Luis Miguel  Delgado Arias; Daniel Alba Ospina; Maria Alejandra  López Ríos

doi:10.30554/archmed.26.2.5667.2026

Between academic efficiency and clinicalreasoning: functional profiles of artificialintelligence use in medical students.

Entre la eficiencia académica y el razonamiento clínico: perfiles funcionales de uso de inteligencia artificial en estudiantes de medicina

Published 2026-10-07

https://doi.org/10.30554/archmed.26.2.5667.2026

Open | Download

PDF (Spanish) FLIP HTML (Spanish) XML (Spanish) ePUB (Spanish)

Issue

Vol. 26 No. 2 (2026): Archivos de Medicina (Manizales)

Section

Research Articles

How to Cite

Delgado Arias, L. M. ., Alba Ospina, D., & López Ríos, M. A. . (2026). Between academic efficiency and clinicalreasoning: functional profiles of artificialintelligence use in medical students. Archivos De Medicina (Manizales), 26(2). https://doi.org/10.30554/archmed.26.2.5667.2026

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

DOI

https://doi.org/10.30554/archmed.26.2.5667.2026

Dimensions

PlumX

How to Cite

Delgado Arias, L. M. ., Alba Ospina, D., & López Ríos, M. A. . (2026). Between academic efficiency and clinicalreasoning: functional profiles of artificialintelligence use in medical students. Archivos De Medicina (Manizales), 26(2). https://doi.org/10.30554/archmed.26.2.5667.2026

Download Citation

license

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Luis Miguel Delgado Arias

Magíster en Educación (Enseñanza). Médico. Universidad de Manizales, Manizales, Caldas, Colombia.

https://orcid.org/0000-0001-5978-2017

Daniel Alba Ospina

Magíster en Ciencias Biomédicas con énfasis en Fisiología. Revive centro de entrenamiento, Manizales, Caldas, Colombia.

https://orcid.org/0009-0000-2378-4619

Maria Alejandra López Ríos

Magister en Proyectos para el Desarrollo Integral de la Infancia la Adolescencia y la Juventud. Universidad de Manizales, Manizales, Colombia

https://orcid.org/0009-0000-2129-8676

Show authors biography

Most read articles by the same author(s)

José Jaime Castaño Castrillón, Daniel Alba Ospina, Ana María Arenas Dávila, Flor Eugenia Botero Ramírez, Sandra Constanza Cañón Buitrago, Heber Armando Cardona Grisales, Daniel Castro Zuluaga, Aura Ocampo Salgado, Daniela Ramírez Gutiérrez, Stress; coping strategies; learning styles; and assertive communication; in a sample of students of the Medicine Program at the University of Manizales; and design of an improve-ment process. 2012 , Archivos de Medicina (Manizales): Vol. 13 No. 2
Luis Miguel Delgado Arias, Luz Yaneth Becerra Salazar, Erysipelothrix rhusiopathiae meningitis: case report , Archivos de Medicina (Manizales): Vol. 14 No. 2
Luis Miguel Delgado Arias, Gloria Milena Betancur Zuleta , Maria Alejandra López Rios, How prepared are future doctors for artificial intelligence?A mixed study of perceived importance, reliability, and classroom obstacles , Archivos de Medicina (Manizales): Vol. 25 No. 1 (2025): Archivos de Medicina (Manizales)
Luis Miguel Delgado Arias, Jesús Eduardo Marulanda López, Juan Carlos Gutierrez Hernandez, Dietary patterns and eating behaviors in medical students: a cross-sectional study at a Colombian university , Archivos de Medicina (Manizales): Vol. 26 No. 1 (2026): Archivos de Medicina (Manizales)
María Alejandra López Ríos, Sara J. Zabaraín Cogollo, Diana Carolina Fandiño Tabares, Juan Carlos Gutierrez, Parenting and inequality: the impact of physical violence against children , Archivos de Medicina (Manizales): Vol. 26 No. 1 (2026): Archivos de Medicina (Manizales)
Daniel Alba Ospina, Jhon Fredy Betancur Pérez, José Henry Osorio, Subjective perception of effort and lactacidemia. Study in young university students , Archivos de Medicina (Manizales): Vol. 25 No. 2 (2025): Archivos de Medicina (Manizales)

Artificial intelligence

medical education

medical students.

clinical reasoning

Introduction: Generative artificial intelligence has been rapidly integrated into medical
education, particularly in study tasks and academic productivity. However, it remains
unclear whether its use extends equally to more complex processes, such as clinical
reasoning. Objective: To characterize and compare the functional profiles of AI use
oriented toward academic efficiency and clinical reasoning among medical students.
Method: A cross-sectional study involving 290 medical students. A self-administered
questionnaire was administered, and two proportional indices of AI use were constructed: one oriented toward academic efficiency and the other toward clinical reasoning.
Descriptive analyses, a paired t-test, and Welch’s ANOVA with post hoc comparisons
were performed. Results: The mean age was 20.4 ± 2.66 years and the GPA was
3.99 ± 0.54. 71.3% reported high frequency of AI use. Use oriented toward academic
efficiency was higher than that oriented toward clinical reasoning (0.66 ± 0.22 vs. 0.46
± 0.34), with a mean difference of 0.20 (95% CI: 0.17–0.24; p < 0.001). Differences
were observed according to academic semester. Conclusions: AI use among medical
students is primarily oriented toward academic efficiency, with variations depending on
the stage of training.

Article visits 0 | PDF visits 0

Downloads

Download data is not yet available.

1. Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023 Feb 9;2(2):e0000198. doi: https://doi.org/10.1371/journal.pdig.0000198.
2. Preiksaitis C, Rose C. Opportunities, challenges, and future directions of generative artificial intelligence in medical education: Scoping review. JMIR Med Educ. 2023 Oct 20;9:e48785. doi: https://doi.org/10.2196/48785.
3. Zhang JS, Yoon C, Williams DKA, Pinkas A. Exploring the usage of ChatGPT among medical students in the United States. J Med Educ Curric Dev. 2024 Jul 10;11:23821205241264695. doi: https://doi.org/10.1177/23821205241264695.
4. Weidener L, Fischer M. Artificial intelligence in medicine: Cross-sectional study among medical students on application, education, and ethical aspects. JMIR Med Educ. 2024 Mar 15;10:e51247. doi: https://doi.org/10.2196/51247.
5. Moldt JA, Festl-Wietek T, Madany Mamlouk A, Nieselt K, Fuhl W, Herrmann-Werner A. Chatbots for future docs: Exploring medical students’ attitudes and knowledge towards artificial intelligence and medical chatbots. Med Educ Online. 2023 Dec 31;28(1):2182659. doi: https://doi.org/10.1080/10872981.2023.2182659.
6. Tangadulrat P, Sono S, Tangtrakulwanich B. Using ChatGPT for clinical practice and medical education: Cross-sectional survey of medical students’ and physicians’ perceptions. JMIR Med Educ. 2023 Nov 2;9:e50658. doi: https://doi.org/10.2196/50658.
7. Levingston H, Anderson MC, Roni MA. From theory to practice: Artificial intelligence (AI) literacy course for first-year medical students. Cureus. 2024 Oct 3;16(10):e70706. doi: https://doi.org/10.7759/cureus.70706.
8. Goh E, Gallo R, Hom J, He Z, Posada J, Peng L, et al. Large language model influence on diagnostic reasoning: A randomized clinical trial. JAMA Netw Open. 2024 Oct 1;7(10):e2440969. doi: https://doi.org/10.1001/jamanetworkopen.2024.40969.
9. Ríos-Hoyo AR, Shan NL, Li A, Pearson AT, Pusztai L, Howard FM. Evaluation of large language models as a diagnostic aid for complex medical cases. Front Med (Lausanne). 2024 May 22;11:1380148. doi: https://doi.org/10.3389/fmed.2024.1380148.
10. Qiu P, Wu C, Liu S, Feng J, Wang J, Zhang Y, et al. Quantifying the reasoning abilities of LLMs on clinical cases. Nat Commun. 2025 Jan 15;16:9799. doi: https://doi.org/10.1038/s41467-025-64769-1.
11. Nguyen T. ChatGPT in medical education: A precursor for automation bias? JMIR Med Educ. 2024 Jan 12;10:e50174. doi: https://doi.org/10.2196/50174.
12. Currie GM. Academic integrity and artificial intelligence: Is ChatGPT hype, hero or heresy? Semin Nucl Med. 2023 Sep;53(5):719-730. doi: https://doi.org/10.1053/j.semnuclmed.2023.04.008.
13. Jaleel A, Aziz RA, Farid GF, Bashir MZ. The impact of ChatGPT on academic integrity in medical education: A developing nation perspective. Front Educ. 2025 Feb 10;10:1554444. doi: https://doi.org/10.3389/feduc.2025.1554444.
14. Russell RG, Lovett Novak L, Patel M, Garvey KV, Craig KJT, Jackson GP, et al. Competencies for the use of artificial intelligence-based tools by health care professionals. Acad Med. 2023 Mar 1;98(3):348-356. doi: https://doi.org/10.1097/ACM.0000000000004963.
15. Singla R, Pupic N, Ghaffarizadeh SA, Kim C, Hu R, Forster BB, et al. Developing a Canadian artificial intelligence medical curriculum using a Delphi study. npj Digit Med. 2024 Nov 14;7:323. doi: https://doi.org/10.1038/s41746-024-01307-1.
16. Oftring ZS, Deutsch K, Tolks D, Jungmann F, Kuhn S. Female medical students’ perception of AI for academic and clinical use: A qualitative interview study. JMIR Med Educ. 2025 Jan 20;11:e65220. doi: https://doi.org/10.2196/65220.
17. Sallam M. ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 2023 Mar 19;11(6):887. doi: https://doi.org/10.3390/healthcare11060887.
18. Masters K. Ethical use of Artificial Intelligence in Health Professions Education: AMEE Guide No. 158. Med Teach. 2023 Jun;45(6):574-584. doi: https://doi.org/10.1080/0142159X.2023.2186203.
19. Darvishi A, Khosravi H, Sadiq S, Gašević D, Siemens G. Impacto de la asistencia de IA en la autonomía del estudiante. Comput Educ. 2023 Dec;210:104967. doi: https://doi.org/10.1016/j.compedu.2023.104967.
20. Araujo-Sandoval O. El impacto de la inteligencia artificial en el aprendizaje activo. Diario Horizonte Nexus. 2024;2(4):43. doi: https://doi.org/10.70881/hnj/v2/n4/43.
21. Gilson A, Safranek C, Huang T, Socrates V, Chi L, Taylor R, et al. ¿Cómo se desempeña ChatGPT en el Examen de Licencia Médica de los Estados Unidos (USMLE)? Las implicaciones de los modelos de lenguaje grandes para la educación médica y la evaluación del conocimiento. JMIR Med Educ. 2023 Feb 1;9:e45312. doi: https://doi.org/10.2196/45312.
22. Song Z, Yue S, Zhang J. Pensar no es una ilusión: Superando las limitaciones de los modelos de razonamiento mediante la ampliación de herramientas. ArXiv [Preprint]. 2025 [citado 2026 Abr 13]:abs/2507.17699. Disponible en: https://doi.org/10.48550/arxiv.2507.17699.
23. Cherrez-Ojeda I, Gallardo-Bastidas JC, Robles-Velasco K, Osorio MF, Vélez León EM, León Velastegui M, et al. Understanding health care students’ perceptions, beliefs, and attitudes toward AI-powered language models: Cross-sectional study. JMIR Med Educ. 2024 Feb 28;10:e51757. doi: https://doi.org/10.2196/51757.
24. Abd-Alrazaq A, AlSaad R, Alhuwail D, Ahmed A, Healy PM, Latifi S, et al. Large language models in medical education: Opportunities, challenges, and future directions. JMIR Med Educ. 2023 Jun 1;9:e48291. doi: https://doi.org/10.2196/48291.
25. Liu M, Okuhara T, Chang X, Okada H, Kiuchi T. Rendimiento de ChatGPT en exámenes de licencia médica en países de todo el mundo: una revisión sistemática y protocolo de metaanálisis. PLoS One. 2024 Oct 24;19(10):e0312771. doi: https://doi.org/10.1371/journal.pone.0312771.
26. Kshetri N. Inteligencia artificial en países en desarrollo. IT Prof. 2020 Jul-Aug;22(4):63-68. doi: https://doi.org/10.1109/mitp.2019.2951851.
27. Oftring ZS, Deutsch K, Tolks D, Jungmann F, Kuhn S. Novel blended learning on artificial intelligence for medical students: Qualitative interview study. JMIR Med Educ. 2025 Jan 20;11:e65220. doi: https://doi.org/10.2196/65220.
28. Walter Y. Abrazando el futuro de la Inteligencia Artificial en el aula: la relevancia de la alfabetización en IA, la ingeniería de impulsos y el pensamiento crítico en la educación moderna. Int J Educ Technol High Educ. 2024;21(1):1-29. doi: https://doi.org/10.1186/s41239-024-00448-3.
29. McDonald N, Johri A, Ali A, Collier A. Inteligencia artificial generativa en la educación superior: evidencia de un análisis de políticas y directrices institucionales. Comput Human Behav Artif Hum. 2025;3:100121. doi: https://doi.org/10.1016/j.chbah.2025.100121.
30. Brügge E, Ricchizzi S, Arenbeck M, Keller MN, Schur L, Stummer W, et al. Large language models improve clinical decision making of medical students through patient simulation and structured feedback: A randomized controlled trial. BMC Med Educ. 2024 Dec 12;24(1):1391. doi: https://doi.org/10.1186/s12909-024-06399-7.
31. Thesen T, Park SH. A generative AI teaching assistant for personalized learning in medical education. npj Digit Med. 2025 Mar 5;8:627. doi: https://doi.org/10.1038/s41746-025-02022-1.