###### Abstract  
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 reportsUniversities worldwide, including those in Spain, have adopted the OSCE over the years, although its implementation by faculty may vary slightly. For years, the OSCE has been used to evaluate the acquisition of clinical competencies at the Teaching Unit of the 12 de Octubre and Infanta Cristina University Hospitals, part of the Faculty of Medicine at the Complutense University of Madrid. Due to the dual qualitative and quantitative nature of this assessment, faculty members must invest significant effort in its development, particularly when providing students with personalized feedback. Exploring ways to optimize this process has been an area of significant research interest to which our teaching unit has made notable contributions [5]. Clinical report writing, a fundamental aspect of daily medical practice, is evaluated in OSCEs. This process not only assesses specific competencies but also prepares students for the critical skill of transmitting clinical information about patients precisely and systematically. However, grading these clinical reports presents significant challenges. Students must document patient history, physical examination findings, test interpretation, and diagnostic and treatment plans, resulting in unstructured data that must be reviewed using pre-designed checklists [1; 2; 4; 5]. The evaluator's role is crucial. To standardize the grading process, a pre-designed checklist created by the teaching team is typically used. Evaluators read the reports written by students and check off items on this checklist, ensuring that each required element is accounted for during the grading process. Beyond this structured approach, evaluators must also be subject-matter experts capable of accurately assessing the student's writing, especially given the various ways a concept can be articulated and the frequent use of acronyms and abbreviations by students. These issues primarily arise from human factors that affect evaluators' performance, introducing subjectivity and variability into the results [2; 6; 7; 8]. Manual grading poses significant challenges, including evaluator fatigue. Over time, the repetitive nature of the task may lead to diminished attention to detail, resulting in poor decision-making and inconsistent grading. This variability undermines the exam's fairness, as evaluations can become overly lenient or excessively strict, compromising the validity of the results [6; 9; 10]. The inherent subjectivity of manual grading is another major concern. Even with efforts to standardize criteria for evaluation, various evaluators may have varying interpretations of the responses from students. The evaluator's emotional state at the moment of grading, their personal interpretation of the responses, or their past experiences can all have an impact on this subjectivity [8; 9]. Manual grading also involves prolonged grading times, which not only delay the delivery of results but also increase the operational costs of OSCEs [11]. These challenges highlight the urgent need for alternative methods to improve grading accuracy and efficiency. For decades, artificial intelligence (AI) has transformed various fields, including medical education. As early as the 1950s, researchers began exploring how machines could perform cognitively demanding tasks such as language processing and decision-making. However, it is only in the past ten years that AI, particularly in the area of natural language processing (NLP), has advanced to the point where it can be practically applied in complex fields like medical education [12; 13; 14; 15; 16].