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INTRODUCTION: The healthcare sector is a major contributor to greenhouse gas emissions, accounting for 8 % of annual French emissions. Eco-design in healthcare, which provides care with equal quality, safety, and relevance but with a lower environmental impact, is therefore a crucial lever for sustainable medical practice. This article explores the application of eco-design in anatomical and cytopathological practices (ACP) in France, in response to the country's decarbonization goals. OBJECTIVES: After demonstrating that decarbonization is possible through the chosen eco-design of care and practices in ACP, we describe the barriers to these changes and the potential real-world solutions. DISCUSSION: We examine the challenges and solutions for integrating eco-design principles into daily ACP practice, highlighting the importance of the relevance of medical procedures to reduce unnecessary practices. We discuss the technical and human barriers in ACP, as well as the solutions: raising awareness among laboratory personnel, industrial stakeholders, research and innovation, the involvement of scientific societies, and initiatives from the collective for Ecological Transformation in ACP (TEAP). Finally, we propose financial incentives to make eco-friendly practices economically viable in ACP. CONCLUSION: Eco-design in ACP practices is essential to address the climate challenge and ensure the sustainability of the healthcare system.

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Global warming and the disruption in ecosystems have been identified as the greatest threats to human health in the 21st century. Today, the French healthcare system accounts for 6.6% to 10% of overall greenhouse gas emissions in France. This system is currently not resilient and totally dependent on fossil fuels. Therefore, a transformation of the current system is needed in order to reduce the deterioration of populations' health. Medical education and pedagogy have been identified as a major solution for the ecological transformation of the healthcare system. The introduction of early education on ecology and environmental health in the first and second cycles of medical studies is a major lever for action. From the third cycle of medical studies, and more specifically in pathology, it is essential to teach this topic to residents and experienced pathologists, whether in "theoretical teaching" or "applied to the medical specialty". The aim of this review is to identify the educational programs and training currently available in the medical courses and at the post-graduate level, regarding ecology/environmental health and the consequences on human health. Then, we will detail more specifically the pedagogical perspectives and training opportunities for pathology residents and pathologists.

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BACKGROUND: Pathology laboratory classes are traditionally conducted using a conventional light microscope. The Coronavirus Disease 2019 (COVID-19) pandemic and recent technological advances necessitated remote learning through online classes using virtual slides (VS) instead of glass slides (GS). AIM: The purpose of this study was to gauge the perception of learning pathology using virtual slides (VS) as opposed to glass slides (GS) for medical students in Saudi Arabia. This study would help modify teaching methods with the advancement of the application of newer methods in online teaching. METHODS: This two-phased study evaluated learning outcomes and perceptions in pathology online education for medical students. Using a questionnaire, Phase one analyzed second and third-year students' perceptions of the teaching methods after an online pathology course. Phase Two assessed the learning outcomes of third-year students during online practical sessions using a pretest and post-test design. Statistical data were collected using a simple additive approach. Statistical tools were used to determine the factors affecting students' perceptions. RESULTS: The accessibility of VS at any possible time, location, or device was the most advantageous trait of virtual learning (mean = 2.94±0.9). Students agreed the least with virtual slides as the only optimal method of learning pathology (mean = 2.25±0.9). Most enjoyed the virtual lab experience (51.7%) but still prefer both laboratory-GS and virtual-VS classes (83.5%). CONCLUSIONS: VS had the benefit of accessibility and efficiency. The acceptance of VS was significantly affected by the orientation prior to the online class. Findings showed that VS cannot completely replace GS and more aspects such as technical difficulties and prior VS experience should be explored.

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Formalin is the international gold-standard fixative in pathology laboratories. However it is not the ideal one considering its deleterious effects on individuals and the environment. Complete formalin removal or even substitution does not seem possible in the near future. In this update, we present various tools allowing to integrate the use of formalin into an ecocare approach. Among them, formalin recycling according to the protocol developed by the University Hospital of Bordeaux is simple to implement and delivers rapid and significant results, allowing pathology professionals to meet the sustainable development objectives included in the France 2030 agenda.

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BACKGROUND: Mnemonic techniques are memory aids that could help improve memory encoding, storage, and retrieval. Using the brain's natural propensity for pattern recognition and association, new information is associated with something familiar, such as an image, a structure, or a pattern. This should be particularly useful for learning complex medical information. Collaborative documents have the potential to revolutionize online learning because they could increase the creativity, productivity, and efficiency of learning. The purpose of this study was to investigate the feasibility of combining peer creation and sharing of mnemonics with collaborative online documents to improve pathology education. METHODS: We carried out a prospective, quasi-experimental, pretest-posttest pilot study. The intervention group was trained to create and share mnemonics in collaborative documents for pathological cases, based on histopathological slides. The control group compared analog and digital microscopy. RESULTS: Both groups consisted of 41 students and did not reveal demographic differences. Performance evaluations did not reveal significant differences between the groups' pretest and posttest scores. Our pilot study revealed several pitfalls, especially in instructional design, time on task, and digital literacy, that could have masked possible learning benefits. CONCLUSIONS: There is a gap in evidence-based research, both on mnemonics and on CD in pathology didactics. Even though, the combination of peer creation and sharing of mnemonics is very promising from a cognitive neurobiological standpoint, and collaborative documents have great potential to promote the digital transformation of medical education and increase cooperation, creativity, productivity, and efficiency of learning. However, the incorporation of such innovative techniques requires meticulous instructional design by teachers and additional time for students to become familiar with new learning methods and the application of new digital tools to promote also digital literacy. Future studies should also take into account validated high-stakes testing for more reliable pre-posttest results, a larger cohort of students, and anticipate technical difficulties regarding new digital tools.

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The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pathology, a rapidly evolving domain demanding intricate contextual understanding. The restricted domain-specific efficiency of LLMs necessitates the advent of tailored AI tools, as illustrated by advancements seen in the last few years including FrugalGPT and BioBERT. Our initiative in digital pathology emphasises the potential of domain-specific AI tools, where a curated literature database coupled with a user-interactive web application facilitates precise, referenced information retrieval. Motivated by the success of this initiative, we discuss how domain-specific approaches substantially minimise the risk of inaccurate responses, enhancing the reliability and accuracy of information extraction. We also highlight the broader implications of such tools, particularly in streamlining access to scientific research and democratising access to computational pathology techniques for scientists with little coding experience. This Viewpoint calls for an enhanced integration of domain-specific text-generation AI tools in academic settings to facilitate continuous learning and adaptation to the dynamically evolving landscape of medical research.

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The much-hyped artificial intelligence (AI) model called ChatGPT developed by Open AI can have great benefits for physicians, especially pathologists, by saving time so that they can use their time for more significant work. Generative AI is a special class of AI model, which uses patterns and structures learned from existing data and can create new data. Utilizing ChatGPT in Pathology offers a multitude of benefits, encompassing the summarization of patient records and its promising prospects in Digital Pathology, as well as its valuable contributions to education and research in this field. However, certain roadblocks need to be dealt like integrating ChatGPT with image analysis which will act as a revolution in the field of pathology by increasing diagnostic accuracy and precision. The challenges with the use of ChatGPT encompass biases from its training data, the need for ample input data, potential risks related to bias and transparency, and the potential adverse outcomes arising from inaccurate content generation. Generation of meaningful insights from the textual information which will be efficient in processing different types of image data, such as medical images, and pathology slides. Due consideration should be given to ethical and legal issues including bias.

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Introducción: En un servicio de anatomía patológica se analiza la carga laboral en tiempo médico en función de la complejidad de las muestras recibidas, y se valora su distribución entre los patólogos, presentado un nuevo algoritmo informático que favorece una distribución equitativa. Métodos: Siguiendo las directrices para la «Estimación de la carga de trabajo en citopatología e histopatología (tiempo médico) atendiendo al catálogo de muestras y procedimientos de la SEAP-IAP (2.ª edición)» se determinan las unidades de carga laboral (UCL) por patólogo y UCL global del servicio, la carga media laboral que soporta el servicio (factor MU), el tiempo de dedicación de cada patólogo a la actividad asistencial y el número de patólogos óptimo según la carga laboral del servicio. Resultados: Determinamos 12.197 UCL totales anuales para el patólogo jefe de servicio, así como 14.702 y 13.842 para los patólogos adjuntos, con una UCL global del servicio de 40.742. El factor MU calculado es 4,97. El jefe ha dedicado el 72,25% de su jornada a la asistencia y los adjuntos el 87,09 y 82,01%. El número de patólogos óptimo para el servicio es de 3,55. Conclusiones: Todos los resultados obtenidos demuestran la sobrecarga laboral médica, y la distribución de las UCL entre los patólogos no resulta equitativa. Se propone un algoritmo informático capaz de distribuir la carga laboral de manera equitativa, asociado al sistema de información del laboratorio, y que tenga en cuenta el tipo de muestra, su complejidad y la dedicación asistencial de cada patólogo.(AU)

Introduction: In a pathological anatomy service, the workload in medical time is analyzed based on the complexity of the samples received and its distribution among pathologists is assessed, presenting a new computer algorithm that favors an equitable distribution. Methods: Following the second edition of the Spanish guidelines for the estimation of workload in cytopathology and histopathology (medical time) according to the Spanish Pathology Society-International Academy of Pathology (SEAP-IAP) catalog of samples and procedures, we determined the workload units (UCL) per pathologist and the overall UCL of the service, the average workload of the service (MU factor), the time dedicated by each pathologist to healthcare activity and the optimal number of pathologists according to the workload of the service. Results: We determined 12 197 total annual UCL for the chief pathologist, as well as 14 702 and 13 842 UCL for associate pathologists, with an overall of 40 742 UCL for the whole service. The calculated MU factor is 4.97. The chief pathologist devoted 72.25% of his working day to healthcare activity while associate pathologists dedicated 87.09% and 82.01% of their working hours. The optimal number of pathologists for the service is found to be 3.55. Conclusions: The results demonstrate medical work overload and a non-equitable distribution of UCLs among pathologists. We propose a computer algorithm capable of distributing the workload in an equitable manner. It would be associated with the laboratory information system and take into account the type of specimen, its complexity and the dedication of each pathologist to healthcare activity.(AU)

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Introducción y objetivo: La inteligencia artificial se halla plenamente presente en nuestras vidas. En educación las posibilidades de su uso son infinitas, tanto para alumnos como para docentes. Material y métodos: Se ha explorado la capacidad de ChatGPT a la hora de resolver preguntas tipo test a partir del examen de la asignatura Procedimientos Diagnósticos y Terapéuticos Anatomopatológicos de la primera convocatoria del curso 2022-2023. Además de comparar su resultado con el del resto de alumnos presentados, se han evaluado las posibles causas de las respuestas incorrectas. Finalmente, se ha evaluado su capacidad para realizar preguntas de test nuevas a partir de instrucciones específicas. Resultados: ChatGPT ha acertado 47 de las 68 preguntas planteadas, obteniendo una nota superior a la de la media y mediana del curso. La mayor parte de preguntas falladas presentan enunciados negativos, utilizando las palabras «no», «falsa» o «incorrecta» en su enunciado. Tras interactuar con él, el programa es capaz de darse cuenta de su error y cambiar su respuesta inicial por la correcta. Finalmente, ChatGPT sabe elaborar nuevas preguntas a partir de un supuesto teórico o bien de una simulación clínica determinada. Conclusiones: Como docentes estamos obligados a explorar las utilidades de la inteligencia artificial, e intentar usarla en nuestro beneficio. La realización de tareas que suponen un consumo de tipo importante, como puede ser la elaboración de preguntas tipo test para evaluación de contenidos, es un buen ejemplo. (AU)

Introduction and objective: Artificial intelligence is fully present in our lives. In education, the possibilities of its use are endless, both for students and teachers. Material and methods: The capacity of ChatGPT has been explored when solving multiple choice questions based on the exam of the subject «Anatomopathological Diagnostic and Therapeutic Procedures» of the first call of the 2022-23 academic year. In addition, to comparing their results with those of the rest of the students presented the probable causes of incorrect answers have been evaluated. Finally, its ability to formulate new test questions based on specific instructions has been evaluated. Results: ChatGPT correctly answered 47 out of 68 questions, achieving a grade higher than the course average and median. Most failed questions present negative statements, using the words «no», «false» or «incorrect» in their statement. After interacting with it, the program can realize its mistake and change its initial response to the correct answer. Finally, ChatGPT can develop new questions based on a theoretical assumption or a specific clinical simulation. Conclusions: As teachers we are obliged to explore the uses of artificial intelligence and try to use it to our benefit. Carrying out tasks that involve significant consumption, such as preparing multiple-choice questions for content evaluation, is a good example. (AU)

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