Enhancing medical anatomy education with the integration of virtual reality into traditional lab settings.

In medical education, traditional anatomy labs have relied heavily on the hands-on dissection of cadavers to teach the complex spatial relationships within the human body. However, the advent of virtual reality (VR) technology offers the potential for significantly enhancing this traditional approach by providing immersive, interactive 3D visualizations that can overcome some of the limitations of physical specimens. This study explores the integration of VR into a traditional gross anatomy lab to enrich the learning experience for medical students. Methods included the deployment of a VR application developed to complement the dissection process, featuring detailed 3D models of human anatomy that students could manipulate and explore digitally. Approximately 60 s-year medical students participated in the lab, where they engaged with both traditional dissection and the VR application. Results indicated that the VR integration not only increased engagement and satisfaction but also improved the students' ability to understand anatomical structures and their spatial relationships. Moreover, feedback from students suggested more efficient learning and retention than with traditional methods alone. We conclude that VR technology can significantly enhance medical anatomy education by providing an adjunct to traditional dissection, potentially replacing certain aspects of physical specimens with digital simulations that offer repeatable, detailed exploration without the associated logistical and ethical constraints.

Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation.

Gamification has appeared as an alternative educational methodology to traditional tools. Specifically, in anatomy teaching, multiple technological applications have emerged in response to the difficulties of accessing cadaveric material; however, there is insufficient information about the effects of these applications on the performance achieved by students, or about to the best way to adapt learning to meet their educational needs. In this study, we investigated how teaching human anatomy through a mobile gamified technological tool containing recommendation systems can be combined with a virtual assistant to improve the learning and academic performance of medical students in the Anatomy Department at the Universidad de La Frontera in Temuco, Chile and the Anatomy Department at the Pontificia Universidad Católica de Chile. In total, 131 students participated in the experiment, which was divided into two case studies. The main findings led to the conclusion that gamified components support students in learning anatomy. In addition, the predictions and recommendations provided by the virtual assistant enabled the academic aspects that the students needed to improve to be extracted adequately. Future work is expected to support adaptive learning by incorporating new artificial intelligence in education elements that can generate personalized scenarios for studying anatomy based on the application.

Artificial intelligence in laparoscopic simulation: a promising future for large-scale automated evaluations.

INTRODUCTION: A limitation to expanding laparoscopic simulation training programs is the scarcity of expert evaluators. In 2019, a new digital platform for remote and asynchronous laparoscopic simulation training was validated. Through this platform, 369 trainees have been trained in 14 institutions across Latin America, collecting 6729 videos of laparoscopic training exercises. The use of artificial intelligence (AI) has recently emerged in surgical simulation, showing usefulness in training assessment, virtual reality scenarios, and laparoscopic virtual reality simulation. An AI algorithm to assess basic laparoscopic simulation training exercises was developed. This study aimed to analyze the agreement between this AI algorithm and expert evaluators in assessing basic laparoscopic-simulated training exercises. METHODS: The AI algorithm was trained using 400-bean drop (BD) and 480-peg transfer (PT) videos and tested using 64-BD and 43-PT randomly selected videos, not previously used to train the algorithm. The agreement between AI and expert evaluators from the digital platform (EE) was then analyzed. The exercises being assessed involve using laparoscopic graspers to move objects across an acrylic board without dropping any objects in a determined time (BD < 24 s, PT < 55 s). The AI algorithm can detect object movement, identify if objects have fallen, track grasper clamps location, and measure exercise time. Cohen's Kappa test was used to evaluate the agreement between AI assessments and those performed by EE, using a pass/fail nomenclature based on the time to complete the exercise. RESULTS: After the algorithm was trained, 79.69% and 93.02% agreement were observed in BD and PT, respectively. The Kappa coefficients test observed for BD and PT were 0.59 (moderate agreement) and 0.86 (almost perfect agreement), respectively. CONCLUSION: This first approach of AI use in basic laparoscopic skills simulated training assessment shows promising results, providing a preliminary framework to expand the use of AI to other basic laparoscopic skills exercises.

Minimally invasive tele-mentoring opportunity-the mito project.

BACKGROUND: Simulation training is a validated method for acquiring laparoscopic skills. Training sessions may be sporadic or lack continuity in oversight by instructors since traditional programs mandate in-person teaching and evaluation. This study presents the development, implementation, and results of a novel smartphone application that enables remote teacher-student interaction. This interface is used to complete a validated program that provides learner-specific feedback. Outcomes of training via Lapp were compared to outcomes of traditional in-person training. METHODS: A web-based and mobile iOS and Android application (Lapp) was developed to enable a remote student-teacher interaction. Instructors use Lapp to assess video recorded training sessions of students at distant locations and guide them through the laparoscopic skill course with specific and personalized feedback. Surgical trainees at two remote training centers were taught using Lapp. A control group was assessed during traditional simulation training at the training facility, with in-person feedback. Pre- and post-training performances were video recorded for each trainee and blindly evaluated by two experts using a global rating scale (GRS) and a specific rating scale (SRS). RESULTS: A total of 30 trainees were trained via Lapp and compared with 25 locally taught. Performance in the Lapp group improved significantly after the course in both GRS and SRS scores, from 15 [6-17] to 23 [20-25], and from 12 [11-15] to 18 [15-20], respectively. The results between both groups were comparable. CONCLUSION: Laparoscopic simulation training using a mobile app is as effective as in-person instruction in teaching advanced laparoscopic surgical skills. Lapp provides an effective method of teaching through simulation remotely and may allow expansion of robust simulation training curriculums.

A cloud-based mobile system to improve respiratory therapy services at home.

Chronic respiratory diseases are one of the most prevalent health problems in the world. Treatment for these kind of afflictions often take place at home, where the continuous care of a medical specialist is frequently beyond the economical means of the patient, therefore having to rely on informal caregivers (family, friends, etc.). Unfortunately, these treatments require a deep involvement on their part, which results in a heavy burden on the caregivers' routine and usually end up deteriorating their quality of life. In recent years, mHealth and eHealth applications have gained a wide interest in academia due to new capabilities enabled by the latest advancements in mobile technologies and wireless communication infrastructure. These innovations have resulted in several applications that have successfully managed to improve automatic patient monitoring and treatment and to bridge the distance between patients, caregivers and medical specialists. We therefore seek to move this trend forward by now pushing these capabilities into the field of respiratory therapies in order to assist patients with chronic respiratory diseases with their treatment, and to improve both their own and their caregivers' quality of life. This paper presents a cloud-based mobile system to support and improve homecare for respiratory diseases. The platform described uses vital signs monitoring as a way of sharing data between hospitals, caregivers and patients. Using an iterative research approach and the user's direct feedback, we show how mobile technologies can improve a respiratory therapy and a family's quality of life.

REMOTE ASYNCHRONOUS FEEDBACK FOR UNSUPERVISED LAPAROSCOPIC TRAINING: THE "LAPP" PLATFORM.

BACKGROUND: The advantages of laparoscopic surgery over traditional open surgery have changed the surgical education paradigm in the past 20 years. Among its benefits are an improvement in clinical outcomes and patient safety, becoming the standard in many surgical procedures. However, it encompasses an additional challenge due to the complexity to achieve the desired competency level. Simulation-based training has emerged as a solution to this problem. However, there is a relative scarcity of experts to provide personalized feedback. Technology-Enhanced Learning could be a valuable aid in personalizing the learning process and overcoming geographic and time-related barriers that otherwise would preclude the training to happen. Currently, various educational digital platforms are available, but none of them is able to successfully provide personalized feedback. AIMS: The aim of this study was to develop and test a proof of concept of a novel Technology-Enhanced Learning laparoscopic skills platform with personalized remote feedback. METHODS: The platform "Lapp," a web and mobile cloud-based solution, is proposed. It consists of a web and mobile application where teachers can evaluate remotely and asynchronously exercises performed by students, adding personalized feedback for trainees to achieve a learning curve wherever and whenever they train. To assess the effectiveness of this platform, two groups of students were compared: 130 participants received in-person feedback and 39 participants received remote asynchronous feedback throughout the application. RESULTS: The results showed no significant differences regarding competency levels among both groups. CONCLUSION: A novel Technology-Enhanced Learning strategy consisting of remote asynchronous feedback throughout Lapp facilitates and optimizes learning, solving traditional spatiotemporal limitations.

Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM).

The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical training, particularly the use of large language models (LLMs), in enhancing communication, personalizing feedback, and promoting skill development. We discuss the advancements in simulation-based training, AI-driven assessment tools, video-based assessment systems, virtual reality (VR) and augmented reality (AR) platforms, and the potential role of LLMs in the transcription, translation, and summarization of feedback. Despite the promising opportunities presented by AI integration, several challenges must be addressed, including accuracy and reliability, ethical and privacy concerns, bias in AI models, integration with existing training systems, and training and adoption of AI-assisted tools. By proactively addressing these challenges and harnessing the potential of AI, the future of surgical training may be reshaped to provide a more comprehensive, safe, and effective learning experience for trainees, ultimately leading to better patient outcomes. .

Biomechanical analysis of expert anesthesiologists and novice residents performing a simulated central venous access procedure.

BACKGROUND: Central venous access (CVA) is a frequent procedure taught in medical residencies. However, since CVA is a high-risk procedure requiring a detailed teaching and learning process to ensure trainee proficiency, it is necessary to determine objective differences between the expert's and the novice's performance to guide novice practitioners during their training process. This study compares experts' and novices' biomechanical variables during a simulated CVA performance. METHODS: Seven experts and seven novices were part of this study. The participants' motion data during a CVA simulation procedure was collected using the Vicon Motion System. The procedure was divided into four stages for analysis, and each hand's speed, acceleration, and jerk were obtained. Also, the procedural time was analyzed. Descriptive analysis and multilevel linear models with random intercept and interaction were used to analyze group, hand, and stage differences. RESULTS: There were statistically significant differences between experts and novices regarding time, speed, acceleration, and jerk during a simulated CVA performance. These differences vary significantly by the procedure stage for right-hand acceleration and left-hand jerk. CONCLUSIONS: Experts take less time to perform the CVA procedure, which is reflected in higher speed, acceleration, and jerk values. This difference varies according to the procedure's stage, depending on the hand and variable studied, demonstrating that these variables could play an essential role in differentiating between experts and novices, and could be used when designing training strategies.

Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM) / Inovações no treinamento cirúrgico: explorando o papel da inteligência artificial e dos grandes modelos de linguagem (LLM)

ABSTRACT The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical training, particularly the use of large language models (LLMs), in enhancing communication, personalizing feedback, and promoting skill development. We discuss the advancements in simulation-based training, AI-driven assessment tools, video-based assessment systems, virtual reality (VR) and augmented reality (AR) platforms, and the potential role of LLMs in the transcription, translation, and summarization of feedback. Despite the promising opportunities presented by AI integration, several challenges must be addressed, including accuracy and reliability, ethical and privacy concerns, bias in AI models, integration with existing training systems, and training and adoption of AI-assisted tools. By proactively addressing these challenges and harnessing the potential of AI, the future of surgical training may be reshaped to provide a more comprehensive, safe, and effective learning experience for trainees, ultimately leading to better patient outcomes. .

RESUMO 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.

Remote asynchronous feedback for unsupervised laparoscopic training: the "lapp" platform / Feedback assíncrono remoto para treinamento laparoscópico não supervisionado: a plataforma "lapp"

ABSTRACT BACKGROUND: The advantages of laparoscopic surgery over traditional open surgery have changed the surgical education paradigm in the past 20 years. Among its benefits are an improvement in clinical outcomes and patient safety, becoming the standard in many surgical procedures. However, it encompasses an additional challenge due to the complexity to achieve the desired competency level. Simulation-based training has emerged as a solution to this problem. However, there is a relative scarcity of experts to provide personalized feedback. Technology-Enhanced Learning could be a valuable aid in personalizing the learning process and overcoming geographic and time-related barriers that otherwise would preclude the training to happen. Currently, various educational digital platforms are available, but none of them is able to successfully provide personalized feedback. AIMS: The aim of this study was to develop and test a proof of concept of a novel Technology-Enhanced Learning laparoscopic skills platform with personalized remote feedback. METHODS: The platform "Lapp," a web and mobile cloud-based solution, is proposed. It consists of a web and mobile application where teachers can evaluate remotely and asynchronously exercises performed by students, adding personalized feedback for trainees to achieve a learning curve wherever and whenever they train. To assess the effectiveness of this platform, two groups of students were compared: 130 participants received in-person feedback and 39 participants received remote asynchronous feedback throughout the application. RESULTS: The results showed no significant differences regarding competency levels among both groups. CONCLUSION: A novel Technology-Enhanced Learning strategy consisting of remote asynchronous feedback throughout Lapp facilitates and optimizes learning, solving traditional spatiotemporal limitations.

RESUMO RACIONAL: As vantagens da cirurgia laparoscópica sobre a cirurgia aberta tradicional mudaram o paradigma da educação cirúrgica nos últimos 20 anos, tornando-se o padrão em muitos procedimentos cirúrgicos. No entanto, envolve um desafio adicional devido à complexidade para atingir o nível de competência desejado. O treinamento baseado em simulação surgiu como uma solução. No entanto, há uma relativa escassez de especialistas para fornecer feedback personalizado. A Technology Enhanced Learning pode ser uma ajuda valiosa na personalização do processo de aprendizagem e na superação de barreiras geográficas e temporais que impediriam o treinamento. Atualmente, várias plataformas educacionais estão disponíveis, mas nenhuma delas é capaz de fornecer feedback personalizado. OBJETIVOS: desenvolver e testar uma prova de conceito de uma nova plataforma de habilidades laparoscópicas da Technology Enhanced Learning com feedback remoto personalizado. MÉTODOS: É proposta a plataforma "Lapp", uma solução web e móvel baseada em nuvem. É composta por uma aplicação web em que os professores podem avaliar remotamente e de forma assíncrona exercícios realizados pelos alunos, adicionando feedback personalizado para os formandos alcançarem uma curva de aprendizagem onde e quando treinam. Para avaliar a eficácia desta plataforma, dois grupos de alunos foram comparados. 130 participantes receberam feedback pessoal e 39 participantes receberam feedback remoto assíncrono em todo o aplicativo. RESULTADOS: Os resultados não mostraram diferenças significativas em relação ao nível de competência entre os dois grupos. CONCLUSÕES: Uma nova estratégia Technology Enhanced Learning que consiste em feedback assíncrono remoto em toda a Lapp facilita e otimiza o aprendizado, resolvendo as limitações espaço-temporais tradicionais.

Plataforma de software educativa gamificada: experiencia con estudiantes de anatomía de la Universidad de la Frontera / Gamified educational software platform: experience with anatomy students at Universidad de la Frontera

RESUMEN: La tecnología ha abierto la posibilidad de mejorar los entornos de aprendizaje. Sin embargo, en el ámbito de la educación médica, las herramientas que son utilizadas no entregan evidencias claras sobre si los estudiantes realmente están aprendiendo. Específicamente, en la enseñanza de la anatomía han surgido múltiples aplicaciones para satisfacer la necesidad de acceder a material cadavérico, no obstante, éstas carecen de información enriquecida sobre el rendimiento que alcanzan los estudiantes y del cómo adaptar los aprendizajes según sus necesidades educativas. Así, una de las estrategias que actualmente tiene presencia en este ámbito es la gamificación. Este estudio implementa y utiliza una plataforma de software educativa gamificada basada en sistemas de recomendación y asistentes virtuales, capaz de entregar retroalimentación y estrategias para apoyar la apropiación de conocimiento de anatomía de los estudiantes de la carrera de medicina de la Universidad de La Frontera (UFRO), de la ciudad de Temuco, Chile. Cuarenta y cinco estudiantes participaron del estudio. Éste consistió en la utilización de diversos componentes gamificados con técnicas de inteligencia artificial. Los principales hallazgos de esta experiencia permitieron concluir que la utilización de componentes gamificados para el aprendizaje de la anatomía son un recurso que permite apoyar el aprendizaje de los estudiantes.

SUMMARY: Technology has opened the possibility of improving learning environments. However, in the field of medical education, the tools that are used do not provide clear evidence as to whether students are actually learning. Specifically, in the teaching of anatomy, multiple applications have emerged to satisfy the need to access cadaveric material, nevertheless, these lack enriched information on the performance achieved by students and how to adapt learning according to their educational needs. Thus, one of the strategies currently present in this area is gamification. This study implements and uses a gamified educational software platform based on recommender systems and virtual assistants, capable of delivering feedback and strategies to support the appropriation of anatomy knowledge of medical students at the Universidad de La Frontera (UFRO), in the city of Temuco, Chile. Forty-five students participated in the study. The study consisted in the use of various gamified components with artificial intelligence techniques. The main findings of this experience led to the conclusion that the use of gamified components for learning anatomy is a resource that supports student learning.

Plataforma móvil basada en la nube para mejorar la experiencia de aprendizaje en la anatomía humana / Cloud-based mobile platform to enhance the learning experience in human anatomy

RESUMEN: En la actualidad, los cursos en línea han masificado y modificado la forma en que se enseña. Tanto los MOOC como los SPOC presentan soluciones sólidas para enseñar, e incluso poseen herramientas para fomentar la colaboración. A pesar de esto, las herramientas que poseen no fomentan colaboración efectiva y tampoco tienen una forma de medirla. Por otro lado, en anatomía han surgido múltiples aplicaciones debido a las dificultades de acceso a material cadavérico, sin embargo, éstas carecen de colaboración y no entregan información enriquecida del comportamiento y aprendizaje de los estudiantes. Dado esto, presentamos una plataforma móvil basada en la nube, como estrategia de herramienta educacional, que busca fomentar la colaboración en la enseñanza para estudiantes de anatomía y entregar datos que permiten analizar y mejorar la experiencia de aprendizaje. Esta solución se desarrolló usando como eje central las metodologías ágiles de desarrollo. Para el experimento, 29 voluntarios formaron parte del grupo experimental y otros 99 del grupo de control. Se realizaron 2 pruebas para medir sus conocimientos en áreas específicas de la anatomía. Se obtuvo aumentos de 0,59 % y 2,98 % en el puntaje de las pruebas, además, hubo una disminución en la desviación estándar de 11,434 a 5,216 en la primera prueba, y de 6,623 a 3,514 en la segunda prueba, mostrando mejora en ambos casos para el grupo experimental. Los resultados obtenidos muestran un potencial de mejorar la experiencia de aprendizaje al usar este tipo de herramientas educativas.

SUMMARY: Online courses have become popular nowadays, changing the way different subjects are taught. Both MOOCs and SPOCs present robust solutions for teaching, and even have tools to encourage collaboration. Nevertheless, the tools used do not foster effective collaboration nor can they adequately measure outcomes. On the other hand, multiple applications have emerged in anatomy as a result of the difficulty in accessing cadaveric material, however, these lack the collaborative aspect, and do not provide information on student behavior and learning. Given this, we present a cloud-based mobile platform, as an educational tool strategy, to promote collaboration in teaching for anatomy students and provide data that allows to analyze and improve the learning experience. This solution was developed using agile development methodologies as the central axis. In this study, 29 volunteers were part of the experimental group and another 99 the control group; 2 tests were performed to measure knowledge in specific areas of anatomy. Increases of 0.59 % and 2.98 % were obtained in the test score, in addition, there was a reduction in the standard deviation from 11.434 to 5.216 in the first test, and from 6.623 to 3,514 in the second test, showing improvement in both cases for the experimental group. The results obtained indicate a potential to improve the learning experience when using this type of educational tool.

Impresiones 3D de cortes transversales de un cuerpo humano: un recurso didáctico para el estudio de la anatomía seccional / 3D prints of cross sections of a human body: a teaching resource for the study of sectional anatomy

Los cursos de anatomía constituyen un componente esencial del currículo de medicina, aportando las bases morfológicas para el examen clínico, la interpretación de imágenes médicas y la práctica segura de intervenciones quirúrgicas y procedimientos. Recientemente, la tecnología de impresión 3D ha permitido generar réplicas de disecciones de segmentos corporales a escala real que se utilizan como recursos docentes para el estudio de la anatomía humana, generando así modelos docentes de alta verosimilitud que sirven como alternativa al uso de preparaciones cadavéricas para la docencia anatómica. En este trabajo presentamos los resultados obtenidos al utilizar nuestro kit KAN3D que incluye réplicas físicas de secciones transversales del tronco y de las extremidades y una plataforma que aloja los modelos digitales debidamente rotulados, producto financiado con el proyecto FONDEF IT16I10073. La aplicación de estos productos en docencia señalan que las réplicas de secciones transversales de segmentos corporales presentan una alta verosimilitud en términos de forma, color, topografía y texturas, características que las validan como un excelente recurso docente para la docencia y el aprendizaje de la anatomía seccional humana. El kit KAN3D pone a disposición de los estudiantes de las carreras de la salud recursos de alta verosimilitud, disponibles a libre demanda, que les permita reproducir la experiencia de la actividad práctica de Morfología en el momento y lugar en que ellos se encuentren dispuestos, superando así las limitaciones de acceso a los pabellones de Anatomía y a material cadavérico de calidad.

Anatomy courses constitute an essential component of the medical curriculum, providing the morphological basis for the clinical examination, the interpretation of medical images and the safe practice of surgical interventions and procedures. Recently, 3D printing technology has allowed to generate replicas of dissections of body segments on a real scale that are used as teaching resources for the study of human anatomy, thus generating high-likelihood teaching models that serve as an alternative to the use of cadaveric preparations for Anatomical teaching. In this paper we present the results obtained by using our KAN3D kit that includes physical replicas of cross sections of the trunk and extremities and a platform that houses properly labeled digital models, a product financed with the FONDEF IT16I10073 project. The application of these products in teaching indicate that replicas of cross sections of body segments have a high likelihood in terms of shape, color, topography and textures, characteristics that validate them as an excellent teaching resource for teaching and learning the human sectional anatomy. The KAN3D kit makes available to students of health careers a high-likelihood resources, accesible on demand, that allows them to reproduce the experience of the practical activity of Morphology at the time and place where they are willing, exceeding thus the limitations of access to the Anatomy pavilions and quality cadaveric material.

Anatomicis network: una plataforma de software educativa basada en la nube para mejorar la enseñanza de la anatomía en la educación médica / Anatomicis network: a cloud-based educational software platformto enhance anatomy teaching in medical education

En este artículo, se describe una propuesta novedosa de plataforma de software educativa para mejorar la enseñanza de la anatomía en la educación médica. Con el fin de determinar la utilidad y el impacto de esta plataforma, se desarrollo una experiencia inter institucional entre los años 2016 y 2017, el cual involucró a las Universidades de Antofagasta, Playa Ancha, Austral y Católica de Chile. Los departamentos de anatomía que participaron en esta experiencia utilizaron la plataforma de software educativa para acceder a imágenes anatómicas 2D y 3D, videos y evaluaciones teórico prácticas multimodales en línea, pudiendo realizar con sus estudiantes pruebas de usabilidad. Este proyecto pretende aportar a la enseñanza de la anatomía en los diferentes departamentos de anatomía a lo largo del país.

In this article, we describe a novel proposal of an educational software platform to enhance the anatomy teaching in medical education. In order to determine the usefulness and impact of this platform, between 2016 and 2017, an inter-institutional experience was developed, which included the Universities of Antofagasta, Playa Ancha, Austral and Católica de Chile. The participation of anatomy departments in this experience, used the educational software platform to access 2D and 3D anatomical images and online multimodal practical-theoretical evaluations, being able to perform usability tests with their students. This project aims to improve teaching in the different anatomy departments throughout the country.