Daniel Jimenez

The integration of artificial intelligence (AI) in healthcare has transformed clinical practices and medical education, with technologies like diagnostic algorithms and clinical decision support increasingly incorporated into curricula. However, there is still a gap in preparing future physicians to use these technologies effectively and ethically. This scoping review maps the integration of artificial intelligence (AI) in undergraduate medical education (UME), focusing on curriculum development, student competency enhancement, and institutional barriers to AI adoption. A comprehensive search in PubMed, Scopus, and BIREME included articles from 2019 onwards, limited to English and Spanish publications on AI in UME. Exclusions applied to studies focused on postgraduate education or non-medical fields. Data were analyzed using thematic analysis to identify patterns in AI curriculum development and implementation. A total of 34 studies were reviewed, representing diverse regions and methodologies, including cross-sectional studies, narrative reviews, and intervention studies. Findings revealed a lack of standardized AI curriculum frameworks and notable global discrepancies. Key elements such as ethical training, collaborative learning, and digital competence were identified as essential, with an emphasis on transversal skills that support AI as a tool rather than a standalone subject. This review underscores the need for a standardized, adaptable AI curriculum in UME that prioritizes transversal skills, including digital competence and ethical awareness, to support AI's gradual integration. Embedding AI as a practical tool within interdisciplinary, patient-centered frameworks fosters a balanced approach to technology in healthcare. Further regional research is recommended to develop frameworks that align with cultural and educational needs, ensuring AI integration in UME promotes both technical and ethical competencies.

Introducción: El síndrome de Apert (SA), o acrocefalosindactilia tipo I, es un trastorno congénito autosómico dominante causado por una mutación en el gen FGFR2, esencial durante el desarrollo embrionario. Se caracteriza por craneosinostosis, hipoplasia del tercio medio facial y sindactilia. Aunque es poco frecuente, su incidencia es de 1/100,000 a 1/160,000 nacidos vivos, con una prevalencia de 1 a 9 casos por cada 100,000 personas. El diagnóstico prenatal es complejo debido a su similitud con otros síndromes congénitos. Objetivo: Este artículo analiza la caracterización genética y molecular de una paciente diagnosticada con Síndrome de Apert. Metodología: Se obtuvo consentimiento informado y se realizó la historia clínica y examen físico. Se recolectaron muestras de sangre para exámenes paraclínicos y moleculares, así como estudios de imagen. Resultados: Los resultados mostraron características fenotípicas compatibles con el síndrome, como estrabismo, proptosis y craneosinostosis tipo plagiocefalia anterior. Los estudios genéticos detectaron una variante patogénica en el gen FGFR2 (c.755C>G) y una variante de significado incierto (c.532C>T). Discusión: La mutación FGFR2 (c.755C>G) es una de las más frecuentes en el SA y se sugiere que afecta la especificidad del receptor, conduciendo a la supresión de la apoptosis de osteoblastos, resultando en las características fenotípicas del síndrome. Conclusión: Aunque la variante de significado incierto (c.532C>T) presenta características deletéreas, no se han reportado otros casos con esta variante. Actualmente, la paciente no ha presentado deterioro adicional y el pronóstico sigue siendo incierto. Se considera necesario ampliar los estudios moleculares para investigar esta nueva variante y sus implicaciones clínicas.

Introduction: Apert syndrome (AS), or acrocephalosyndactyly type I, is an autosomal dominant congenital disorder caused by a mutation in the FGFR2 gene, essential during embryonic development. It is characterized by craniosynostosis, midface hypoplasia and syndactyly. Although rare, its incidence is 1/100,000 to 1/160,000 live births, with a prevalence of 1 to 9 cases per 100,000 persons. Prenatal diagnosis is complex due to its similarity to other congenital syndromes. Objective: This article analyzes the genetic and molecular characterization of a patient diagnosed with Apert syndrome. Methodology: Informed consent was obtained and a clinical history and physical examination were performed. Blood samples were collected for paraclinical and molecular tests, as well as imaging studies. Results: The results showed phenotypic characteristics compatible with the syndrome, such as strabismus, proptosis and anterior plagiocephaly-type craniosynostosis. Genetic studies detected a pathogenic variant in the FGFR2 gene (c.755C>G) and a variant of uncertain significance (c.532C>T). Discussion: The FGFR2 mutation (c.755C>G) is one of the most frequent in AS and is suggested to affect receptor specificity, leading to suppression of osteoblast apoptosis, resulting in the phenotypic features of the syndrome. Conclusion: Although the variant of uncertain significance (c.532C>T) has deleterious features, no other cases with this variant have been reported. Currently, the patient has not presented additional deterioration and the prognosis remains uncertain. Further molecular studies are considered necessary to investigate this new variant and its clinical implications.

Introduction: Academic fraud is any behavior that gives a student an undeserved advantage over another student. Few studies have explored the causes of and possible solutions to academic fraud in Latin America. We aim to map the knowledge of stakeholders in a Colombian faculty of medicine about the factors that facilitate and prevent academic fraud. Methods: Fuzzy cognitive mapping. We will use the approach proposed by Andersson and Silver to generate fuzzy cognitive maps representing stakeholder knowledge. This process consists of ten steps: (1) definition of the research question; (2) identification of participants; (3) generation of ideas; (4) rationalization of ideas; (5) organization and connection of ideas; (6) weighing; (7) pattern grouping; (8) list of links and digitization; (9) combination of maps and network analysis; and (10) deliberative dialogue. To draw the maps, we will invite medical students, interns, resident physicians, master’s students, and professors in the faculty of medicine. Four medical students will receive training to facilitate the sessions. Participants will identify the factors contributing to academic fraud and their causal relationships. We will use a combination of network analysis and graph theory to identify the chains of factors with greatest influence on academic fraud. Conclusion: The maps will serve to discuss strategies to reduce academic fraud in the Faculty of Medicine and to identify factors that could be addressed in other contexts with similar problems. This research will allow the students who facilitate mapping sessions to learn about research techniques, fuzzy cognitive mapping and academic fraud. Study registration: Registered in OSF Registries on August 2nd, 2022. Registration number: osf.io/v4amz