Application of Artificial Intelligence as an Aid for the Correction of the Objective Structured Clinical Examination: (OSCE)

• Davide Luordo ^[1] ; Marta Torres Arrese ^[3] ; Cristina Tristán Calvo ^[4] ; Kirti Dayal Shani Shani ^[4] ; Luis MIguel Rodríguez Cruz ^[5] ; Francisco Javier García Sánchez ^[1] ; Alfonso Lagares Cómez-Abascal ^[6] ; Rafael Rubio García ^[6] ; Juan Delgado Jiménez ^[6] ; Mercedes Pérez Carreras ^[6] ; Ramiro Díez Lobato ^[6] ; Juan José Granizo Martínez ^[6] ; Yale Tung-Chen ^[2] ; María Victoria Villena Garrido ^[6]
  1. [1] Universidad Complutense de Madrid

     Universidad Complutense de Madrid

     Madrid, España

     
  2. [2] Universidad Autónoma de Madrid

     Universidad Autónoma de Madrid

     Madrid, España

     
  3. [3] 3. Alcorcón Foundation Hospital, 28922 Alcorcón, Spain
  4. [4] 1. Infanta Cristina University Hospital, 28981 Madrid, Spain
  5. [5] 4. Independent Researcher, 28945 Madrid, Spain
  6. [6] 5. 12 de Octubre University Hospital, 28041 Madrid, Spain

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• Localización: fisioGlía: revista de divulgación en Fisioterapia, ISSN-e 2340-6151, Vol. 12, Nº. 2, 2025, págs. 33-41
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • The assessment of clinical competencies is essential in medical training, and the Objective Structured Clinical Examination (OSCE) is an essential tool in this process.

    There are multiple studies exploring the usefulness of artificial intelligence (AI) in medical education. This study explored the use of the GPT-4 AI model to grade clinical reports written by students during the OSCE at the Teaching Unit of the 12 de Octubre and Infanta Cristina University Hospitals, part of the Faculty of Medicineat the Complutense University of Madrid, comparing its results with those of human graders. Ninety-six (96) students participated, and their reports were evaluated by two experts, an inexperienced grader, and the AI using a checklist designed during the OSCE planning by the teaching team.

    The results show a significant correlation between the AI and human graders (ICC = 0.77 for single measures and 0.91 for average measures). AI was more stringent, assigning scores on an average of 3.51 points lower (t = -15.358, p < 0.001); its correction was considerably faster, ting the analysis in only 24 min compared to the 2–4 h required by human graders.

    These results suggest that AI could be a promising tool to enhance efficiency and objectivity in OSCE grading