Browsing by Author "Jarry Trujillo, Cristián Ignacio"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Effectiveness of a Train the Trainers course for digital feedback in healthcare simulation via a remote and asynchronous learning program
    (2024) Montero Jaras, Isabella; Durán Espinoza, Valentina Alexandra; Miguieles Schilling, Mariana Andrea; Belmar Riveros, Francisca Andrea; Figueroa Fernández, Ursula Victoria; Brandon Valencia Coronel; Wiseman Jeffrey; Jarry Trujillo, Cristián Ignacio; Gabriel Escalona Vives; Villagran Gutiérrez, Ignacio Andrés; Corvetto Aqueveque, Marcia Antonia; Varas Cohen, Julián Emanuel
    Introduction With a growing demand for tutoring in medical education, the need for Train the Trainers courses have increased. These courses can be difficult to coordinate between trainer and trainee (trainers in training). This study aimed to evaluate the effectiveness of a digital remote and asynchronous (RA) Train the Trainers (TTT) course compared to an in-person (IP) course. Methods In this quasi-experimental study, we compared an in-person TTT course with a remote and asynchronous TTT course. The course involved theoretical and practical components, and upon completion, the trainees transitioned into instructor roles where they provided feedback on video recordings of third-year medical students performing simulated procedures. Performance of the third-year medical students was analyzed, comparing global rating scores. Data analysis was performed using non-parametric tests considering statistical significance p < 0.05. Results A total of 108 trainers-in-training completed the TTT course; 30 IP and 78 RA. They assessed 1,016 videos. The first attempt score was 17 (14–20) and 19 (15–22) in IP and RA training, respectively with statistically significant differences (p-value = 0.041). On the second attempt, scores were 23 (20–24) and 23 (20–24) in IP and RA training, respectively. This difference was not statistically significant. Conclusion The implementation of a remote and asynchronous TTT course yielded comparable results to the traditional in-person method. This new learning modality facilitated increased platform inputs, saw higher first-attempt scores in students, and did not adversely impact their final competency outcomes.
  • Thumbnail Image
    Item
    Inovações no treinamento cirúrgico: explorando o papel da inteligência artificial e dos grandes modelos de linguagem (LLM)
    (2023) Varas Cohen, Julián Emanuel; Valencia Coronel, Brandon; Villagrán Gutiérrez, Ignacio Andrés; Escalona Vivas, Gabriel Enrique; Hernández Román, Rocío Belén; Schuit Condell, Gregory Kees; Duran Espinoza, Valentina Alexandra; Lagos Villaseca, Antonia Elisa; Jarry Trujillo, Cristián Ignacio; Neyem, Hugo Andrés; Achurra Tirado, Pablo Andrés
    O cenário do treinamento cirúrgico está evoluindo rapidamente com o surgimento da inteligência artificial (IA) e sua integração na educação e simulação. Este artigo explora as aplicações e benefícios potenciais do treinamento cirúrgico assistido por IA, em particular o uso de modelos de linguagem avançados (MLAs), para aprimorar a comunicação, personalizar o feedback e promover o desenvolvimento de habilidades. Discutimos os avanços no treinamento baseado em simulação, ferramentas de avaliação impulsionadas por IA, sistemas de avaliação baseados em vídeo, plataformas de realidade virtual (RV) e realidade aumentada (RA), e o papel potencial dos MLAs na transcrição, tradução e resumo do feedback. Apesar das oportunidades promissoras apresentadas pela integração da IA, vários desafios devem ser abordados, incluindo precisão e confiabilidade, preocupações éticas e de privacidade, viés nos modelos de IA, integração com os sistemas de treinamento existentes, e treinamento e adoção de ferramentas assistidas por IA. Ao abordar proativamente esses desafios e aproveitar o potencial da IA, o futuro do treinamento cirúrgico pode ser remodelado para proporcionar uma experiência de aprendizado mais abrangente, segura e eficaz para os aprendizes, resultando em melhores resultados para os pacientes.
  • No Thumbnail Available
    Item
    Surgical training scalability through AI-based innovations
    (2025) Jarry Trujillo, Cristián Ignacio; Vela Ulloa, Javier Ignacio; Durán Espinoza, Valentina Alexandra; Van Leeuwen, Matthew; Varas, Julián
    Training scalability in surgical education is challenged by the limited availability of expert instructors and the need for personalized feedback. The integration of artificial intelligence (AI) into surgical training offers promising solutions to these challenges. This narrative review examines how AI-based tools enhance surgical training scalability, focusing on automated assessments, feedback delivery, and simulation-based education. A comprehensive literature search identified relevant studies on AI applications in surgical training. The review discusses the educational foundations of simulation training, defines AI and its subsets (machine learning and deep learning), and explores the significance of phase/task segmentation in surgical procedures. It critically analyzes current AI applications in automated assessment and feedback, highlighting impediments to scalability such as the specificity of AI models to particular procedures and the need for new models across different domains. The findings suggest that while AI holds significant potential for improving surgical education, challenges remain in generalizing models and integrating AI tools into diverse training contexts.