El papel emergente de la inteligencia artificial en los exámenes clínicos objetivos estructurados

Nelson Luis Cahuapaza-Gutierrez; Cielo Cinthya Calderon-Hernandez

doi:10.21142/mecp.2026.1.3.e032

Autores/as

Nelson Luis Cahuapaza-Gutierrez Universidad Científica del Sur, Lima, Perú; Research Department, N y C – Center of Research and Medical Excellence (CRME), Lima, Perú. https://orcid.org/0000-0002-1228-1941
Cielo Cinthya Calderon-Hernandez Universidad Científica del Sur, Lima, Perú; Research Department, N y C – Center of Research and Medical Excellence (CRME), Lima, Perú. https://orcid.org/0009-0004-5242-3141

DOI:

https://doi.org/10.21142/mecp.2026.1.3.e032

Palabras clave:

Inteligencia Artificial, Examen Físico, Educación Médica

Resumen

La evaluación de las competencias clínicas es fundamental en la formación médica, y el Examen Clínico Objetivo Estructurado (ECOE) constituye una herramienta ampliamente utilizada para este propósito. El desarrollo de la inteligencia artificial (IA) ha generado interés en su aplicación en los diferentes componentes del ECOE. En este contexto, se revisó la literatura reciente para sintetizar la evidencia disponible sobre sus aplicaciones. Los hallazgos sugieren que los modelos de IA, particularmente los grandes modelos de lenguaje podrían apoyar la enseñanza, el aprendizaje y la evaluación mediante la generación y resolución de estaciones, la calificación de evaluaciones, la provisión de retroalimentación y la mejora de habilidades clínicas para el ECOE. Sin embargo, presentan limitaciones para evaluar aspectos complejos de la comunicación clínica, como la comunicación no verbal, la empatía y el juicio clínico. Por ello, su uso debe considerarse complementario a la experiencia docente y clínica.

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Referencias

1. Rayyan MR. The use of objective structured clinical examination in dental education- a narrative review. Front Oral Health. 2024;5:1336677. doi: 10.3389/froh.2024.1336677.

2. Barnes KN, Hardinger K, Hanson K, Gortney J. The Price of Preparedness: An Economic Evaluation of an OSCE in Pharmacy Training. Am J Pharm Educ. 2026;90(6):101990. doi: 10.1016/j.ajpe.2026.101990.

3. Weaver SB, Daftary M, Wingate L, Turner M. Evaluation of faculty inter-variability OSCE grade scoring on overall student performance in a laboratory course. Pharm Educ. 2022;22(1):48-53. doi: 10.46542/pe.2022.221.4853.

4. Sudan R, Clark P, Henry B. Cost and logistics for implementing the American College of Surgeons objective structured clinical examination. Am J Surg. 2015;209(1):140-4. doi: 10.1016/j.amjsurg.2014.10.001.

5. Dean J. A Golden Decade of Deep Learning: Computing Systems & Applications. Daedalus. 2022;151(2):58-74. doi: 10.1162/daed_a_01900.

6. Rejeleene R, Mehta NB. Artificial intelligence in medicine: How it works, how it fails. Cleve Clin J Med. 2026;93(2):113-20. doi: 10.3949/ccjm.93a.25089.

7. Occhipinti A, Verma S, Doan LMT, Angione C. Mechanismaware and multimodal AI: beyond model-agnostic interpretation. Trends Cell Biol. 2024;34(2):85-9. doi: 10.1016/j. tcb.2023.11.002.

8. Kathiyan T, Kishore M, Priyanka D. An Intelligent AI-Based Framework for Automated Educational Video Generation. 2026 International Conference on Electronic Systems and Intelligent Computing (ICESIC), Chennai, India; 2026. pp. 373-378, doi: 10.1109/ICESIC67389.2026.11496457.

9. Woitsch R, Muck C, Utz W, Zeiner H. Enable Flexibilisation in FAIRWork’s Democratic AI-based Decision Support System by Applying Conceptual Models Using ADOxx. Complex Systems Informatics and Modeling Quarterly. 2024;(38):27– 53. doi: 10.7250/csimq.2024-38.02.

10. Flores-Núñez AM, Rivas-Sucari HC, Manrique-Rabelo CM, Rivas-Sucari SA, Flores-Núñez AM, Rivas-Sucari HC, et al. El impacto de la inteligencia artificial en la educación médica: aplicaciones, beneficios y desafíos. Gac Med Mex. 2026;162(2):194-8. doi: 10.24875/gmm.25000069.

11. Zafar I, Aboueisha H, Elhassan I, Shersad F, Caliskan SA, Syeda AF, et al. Ten tips for utilizing AI to generate high quality OSCE stations in medical education. Front Med (Lausanne). 2025;12:1744657. doi: 10.3389/fmed.2025.1744657.

12. Borg A, Schiött J, Ivegren W, Gentline C, Huss V, Hugelius AM, et al. AI-generated Feedback Following Social Robotic Virtual Patient Interactions and Medical Student Performance: Nonrandomized Quasi-Experimental Study. JMIR Med Educ. 2026;12:e90368. doi: 10.2196/90368.

13. Yokose M, Hirosawa T, Sakamoto T, Kawamura R, Suzuki Y, Harada Y, et al. The Validity of Generative Artificial Intelligence in Evaluating Medical Students in Objective Structured Clinical Examination: Experimental Study. JMIR Form Res. 2025;9:e79465. doi: 10.2196/79465.

14. Dai X, Gong Y, Ma LY. Effects of ChatGPT-generated immediate feedback integrated into VR-based OSCEs on nursing students’ performance: a randomized crossover study. BMC Nurs. 2026;25:396. doi: 10.1186/s12912-026-04633-9.

15. Wang XJ, Song LJ, Jiao XP, Chen SQ. Integration of Artificial Intelligence in Nursing Clinical Education: Enhancing the Mini-CEX Model. J Multidiscip Healthc. 2025;18:7327-37. doi: 10.2147/JMDH.S550145.

16. Vázquez JT, Acosta HP, Loya JL. Integral evaluation of the medical graduate profile using the innovated ECOE-M. Educ Méd. 2026;27(3):101183. doi: 10.1016/j.edumed.2026.101183.

17. Chastain A, Schempp A. How Artificial Intelligence Can Affect Physician Assistant Student Self-Efficacy When Preparing for Objective Structured Clinical Examinations. J Physician Assist Educ. 2025;36(4):435-8. doi: 10.1097/ JPA.0000000000000692.

18. Hicke Y, Geathers J, Vu K, Sewell J, Cardie C, Talwalkar J, et al. MedSimAI: Simulation and Formative Feedback Generation to Enhance Deliberate Practice in Medical Education. In: Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference. 2026. doi: 10.1145/3785022.3785092.

19. Esmail S, Concannon B. Immersive Virtual Reality and AI (Generative Pretrained Transformer) to Enhance Student Preparedness for Objective Structured Clinical Examinations: Mixed Methods Study. JMIR Serious Games. 2025;13:e69428. doi:10.2196/69428.

20. Ali M, Rehman S, Cheema E. Impact of artificial intelligence on the academic performance and test anxiety of pharmacy students in objective structured clinical examination: a randomized controlled trial. Int J Clin Pharm. 2025;47(4):1034- 41. doi:10.1007/s11096-025-01876-5.

21. Mishra GV, Luharia AA, Naqvi W, Sood A. Artificial Intelligence in OSCE: Innovations and Implications for Medical Education Assessment – A Systematic Review. In: 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). 2024. doi: 10.1109/IDICAIEI61867.2024.10842789.

22. Jones S, Axman LM, Widemark E, Bafaloukos C, Sabado ET, Cranch-Kaniut C, et al. Enhancing the Objective Structured Clinical Examination Using Artificial Intelligence. J Dr Nurs Pract. 2025;JDNP-2025-0061.R1. doi: 10.1891/JDNP-2025-0061.

23. Zouakia Z, Logak E, Szymczak A, Jais JP, Burgun A, Tsopra R. AI-Driven Objective Structured Clinical Examination Generation in Digital Health Education: Comparative Analysis of Three GPT-4o Configurations. JMIR Med Educ. 2026;12:e82116. doi: 10.2196/82116.

24. Li SW, Kemp MW, Logan SJS, Dimri PS, Singh N, Mattar CNZ, et al. ChatGPT outscored human candidates in a virtual objective structured clinical examination in obstetrics and gynecology. Am J Obstet Gynecol. 2023;229(2):172.e1-172.e12. doi: 10.1016/j.ajog.2023.04.020.

25. Tekin M, Yurdal MO, Toraman Ç, Korkmaz G, Uysal İ. Is AI the future of evaluation in medical education?? AI vs. human evaluation in objective structured clinical examination. BMC Med Educ. 2025;25(1):641. doi: 10.1186/s12909-025-07241-4.

26. Yamamoto A, Koda M, Ogawa H, Miyoshi T, Maeda Y, Otsuka F, et al. Enhancing Medical Interview Skills Through AISimulated Patient Interactions: Nonrandomized Controlled Trial. JMIR Med Educ. 2024;10:e58753. doi: 10.2196/58753.

27. Liu M, Okuhara T, Chang X, Shirabe R, Nishiie Y, Okada H, et al. Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis. J Med Internet Res. 2024;26:e60807. doi: 10.2196/60807.

28. Li PY, Gershon A, Kouri A. Evaluating the Accuracy of Large Language Models in Answering Asthma Multiple Choice and Objective Structured Clinical Examination Questions. Chest. 2026;169(5):1286-97. doi: 10.1016/j.chest.2025.11.034.

29. Sethi MIS, Suhas S, Harbishettar V, Manjunatha N, Kumar CN, Bharadwaj R, et al. Benchmarking Large Language Models Against Psychiatry Residents Using Traditional Institutional Assessments. Indian J Psychol Med. 2026;02537176261435658. doi: 10.1177/02537176261435658.

30. Szychowiak P, Wong-So J, Messet H, Faure M, Breteau I, Jamard S, et al. Large language model-generated versus teacher-written objective structured clinical examination stations for medical students: a blinded comparative pilot study. J Educ Eval Health Prof. 2026;23:9. doi: 10.3352/ jeehp.2026.23.9.

31. Sourial M, Hagler JC. A Pilot Study Using Artificial Intelligence to Enhance Efficiency, Accuracy, and Objectivity in Grading Pharmacy Objective Structured Clinical Examinations. Am J Pharm Educ. 2025;89(8):101455. doi: 10.1016/j.ajpe.2025.101455.

32. Geathers J, Hicke Y, Chan C, Rajashekar N, Young S, Sewell J, et al. Benchmarking Generative AI for Scoring Medical Student Interviews in Objective Structured Clinical Examinations (OSCEs). In: Cristea AI, Walker E, Lu Y, Santos OC, Isotani S, editors. Artificial Intelligence in Education. Cham: Springer Nature Switzerland; 2025. p. 231-45. doi: 10.1007/978-3-031-98420-4_17.

33. Luordo D, Arrese MT, Calvo CT, Shani KDS, Cruz LMR, Sánchez FJG, et al. Application of Artificial Intelligence as an Aid for the Correction of the Objective Structured Clinical Examination (OSCE). Appl Sci. 2025;15(3). doi: 10.3390/ app15031153.

34. Thomas K, Szalacha L, Hanna K, Anibal J, Petrilli J. Evaluating the Effectiveness of ChatGPT Versus Human Proctors in Grading Medical Students’ Post-OSCE Notes. Fam Med. 2025;57(10):727-31. doi: 10.22454/FamMed.2025.954255.

35. Alshareef N, Giga S, Fletcher I. Test anxiety, emotional regulation and academic performance among medical students: a qualitative study. Med Educ Online. 2025;30(1):2505177. doi: 10.1080/10872981.2025.2505177.

36. Daniels VJ, Ortiz S, Sandhu G, Lai H, Yoon MN, Bulut O, et al. Effect of Detailed OSCE Score Reporting on Learning and Anxiety in Medical School. J Med Educ Curric Dev. 2021;8:2382120521992323. doi: 10.1177/2382120521992323.

37. Lee HY, Kim J, Choi H, Bae H, Jeong A, Choi S, et al. Comparing AI chatbot simulation and peer role-play for OSCE preparation: a pilot randomized controlled trial. BMC Med Educ. 2025;25(1):1755. doi: 10.1186/s12909-025-08308-y.

38. Schleicher I, Leitner K, Juenger J, Moeltner A, Ruesseler M, Bender B, et al. Examiner effect on the objective structured clinical exam - a study at five medical schools. BMC Med Educ. 2017;17(1):71. doi: 10.1186/s12909-017-0908-1.

39. Claridge H, Stone K, Ussher M. The ethnicity attainment gap among medical and biomedical science students: a qualitative study. BMC Med Educ. 2018;18(1):325. doi: 10.1186/ s12909-018-1426-5.

40. Chong L, Taylor S, Haywood M, Adelstein BA, Shulruf B. The sights and insights of examiners in objective structured clinical examinations. J Educ Eval Health Prof. 2017;14:34. doi: 10.3352/jeehp.2017.14.34.

41. Hyde S, Fessey C, Boursicot K, MacKenzie R, McGrath D. OSCE rater cognition - an international multi-centre qualitative study. BMC Med Educ. 2022;22(1):6. doi: 10.1186/ s12909-021-03077-w.

42. Myung SJ, Kim JW, Kim CW, Kim DH, Eo E, Kim JH, et al. Effect of limiting checklist on the validity of objective structured clinical examination: A comparative validity study. Med Teach. 2025;47(8):1360-6. doi: 10.1080/0142159X.2024.2430364.