Utility of artificial intelligence for detection of pneumothorax on chest radiopgraphs done after transthoracic percutaneous transthoracic biopsy guided by computed tomography.

OBJETIVE: To assess the ability of an artificial intelligence software to detect pneumothorax in chest radiographs done after percutaneous transthoracic biopsy. MATERIAL AND METHODS: We included retrospectively in our study adult patients who underwent CT-guided percutaneous transthoracic biopsies from lung, pleural or mediastinal lesions from June 2019 to June 2020, and who had a follow-up chest radiograph after the procedure. These chest radiographs were read to search the presence of pneumothorax independently by an expert thoracic radiologist and a radiodiagnosis resident, whose unified lecture was defined as the gold standard, and the result of each radiograph after interpretation by the artificial intelligence software was documented for posterior comparison with the gold standard. RESULTS: A total of 284 chest radiographs were included in the study and the incidence of pneumothorax was 14.4%. There were no discrepancies between the two readers' interpretation of any of the postbiopsy chest radiographs. The artificial intelligence software was able to detect 41/41 of the present pneumothorax, implying a sensitivity of 100% and a negative predictive value of 100%, with a specificity of 79.4% and a positive predictive value of 45%. The accuracy was 82.4%, indicating that there is a high probability that an individual will be adequately classified by the software. It has also been documented that the presence of Port-a-cath is the cause of 8 of the 50 of false positives by the software. CONCLUSIONS: The software has detected 100% of cases of pneumothorax in the postbiopsy chest radiographs. A potential use of this software could be as a prioritisation tool, allowing radiologists not to read immediately (or even not to read) chest radiographs classified as non-pathological by the software, with the confidence that there are no pathological cases.

Artificial intelligence in paediatrics: Current events and challenges.

This article examines the use of artificial intelligence (AI) in the field of paediatric care within the framework of the 7P medicine model (Predictive, Preventive, Personalized, Precise, Participatory, Peripheral and Polyprofessional). It highlights various applications of AI in the diagnosis, treatment and management of paediatric diseases as well as the role of AI in prevention and in the efficient management of health care resources and the resulting impact on the sustainability of public health systems. Successful cases of the application of AI in the paediatric care setting are presented, placing emphasis on the need to move towards a 7P health care model. Artificial intelligence is revolutionizing society at large and has a great potential for significantly improving paediatric care.

ChatGPT's learning and reasoning capacity in anesthesiology

Introduction: Over the past few months, ChatGPT has raised a lot of interest given its ability to perform complex tasks through natural language and conversation. However, its use in clinical decision-making is limited and its application in the field of anesthesiology is unknown. Objective: To assess ChatGPT's basic and clinical reasoning and its learning ability in a performance test on general and specific anesthesia topics. Methods: A three-phase assessment was conducted. Basic knowledge of anesthesia was assessed in the first phase, followed by a review of difficult airway management and, finally, measurement of decision-making ability in ten clinical cases. The second and the third phases were conducted before and after feeding ChatGPT with the 2022 guidelines of the American Society of Anesthesiologists on difficult airway management. Results: On average, ChatGPT succeded 65% of the time in the first phase and 48% of the time in the second phase. Agreement in clinical cases was 20%, with 90% relevance and 10% error rate. After learning, ChatGPT improved in the second phase, and was correct 59% of the time, with agreement in clinical cases also increasing to 40%. Conclusions: ChatGPT showed acceptable accuracy in the basic knowledge test, high relevance in the management of specific difficult airway clinical cases, and the ability to improve after learning.

Introducción: En los últimos meses, ChatGPT ha suscitado un gran interés debido a su capacidad para realizar tareas complejas a través del lenguaje natural y la conversación. Sin embargo, su uso en la toma de decisiones clínicas es limitado y su aplicación en el campo de anestesiología es desconocido. Objetivo: Evaluar el razonamiento básico, clínico y la capacidad de aprendizaje de ChatGPT en una prueba de rendimiento sobre temas generales y específicos de anestesiología. Métodos: Se llevó a cabo una evaluación dividida en tres fases. Se valoraron conocimientos básicos de anestesiología en la primera fase, seguida de una revisión del manejo de vía aérea difícil y, finalmente, se midió la toma de decisiones en diez casos clínicos. La segunda y tercera fases se realizaron antes y después de alimentar a ChatGPT con las guías de la Sociedad Americana de Anestesiólogos del manejo de la vía aérea difícil del 2022. Resultados: ChatGPT obtuvo una tasa de acierto promedio del 65 % en la primera fase y del 48 % en la segunda fase. En los casos clínicos, obtuvo una concordancia del 20 %, una relevancia del 90 % y una tasa de error del 10 %. Posterior al aprendizaje, ChatGPT mejoró su tasa de acierto al 59 % en la segunda fase y aumentó la concordancia al 40 % en los casos clínicos. Conclusiones: ChatGPT demostró una precisión aceptable en la prueba de conocimientos básicos, una alta relevancia en el manejo de los casos clínicos específicos de vía aérea difícil y la capacidad de mejoría secundaria a un aprendizaje.

Artificial intelligence, applications and challenges in simulation-based education

The rapid advancement of Artificial Intelligence (AI) has taken the world by "surprise" due to the lack of regulation over this technological innovation which, while promising application opportunities in different fields of knowledge, including education, simultaneously generates concern, rejection and even fear. In the field of Health Sciences Education, clinical simulation has transformed educational practice; however, its formal insertion is still heterogeneous, and we are now facing a new technological revolution where AI has the potential to transform the way we conceive its application.

El rápido avance de la inteligencia artificial (IA) ha tomado al mundo por "sorpresa" debido a la falta de regulación sobre esta innovación tecnológica, que si bien promete oportunidades de aplicación en diferentes campos del conocimiento, incluido el educativo, también genera preocupación e incluso miedo y rechazo. En el campo de la Educación en Ciencias de la Salud la Simulación Clínica ha transformado la práctica educativa; sin embargo, aún es heterogénea su inserción formal, y ahora nos enfrentamos a una nueva revolución tecnológica, en la que las IA tienen el potencial de transformar la manera en que concebimos su aplicación.

Consideraciones Éticas y Académicas sobre el Uso de Inteligencia Artificial en Cursos de Metodología de la Investigación en Rehabilitación / Ethical and Academic Reflections on the Integration of Artificial Intelligence in Research Methodology Courses in Rehabilitation Science

En el cruce entre la revolución tecnológica y la educación en ciencias de la rehabilitación y del movimiento humano, la inteligencia artificial (IA) emerge como herramienta transformadora en los cursos de metodología de investigación. Este artículo destaca su potencial para optimizar la experiencia de aprendizaje y personalizar la instrucción, pero enfatiza la necesidad crucial de abordar desafíos éticos y pedagógicos. Propone orientaciones para equilibrar la innovación educativa y la responsabilidad académica, resaltando la importancia de la implementación consciente y planificada de la IA en los equipos de investigación en ciencias de la rehabilitación y del movimiento humano, garantizando así la integridad científica y ética en este campo en constante evolución.

In the intersection between technological advancements and education in rehabilitation science, artificial intelligence (AI) emerges as a transformative tool in research methodology. This article navigates the ethical and academic considerations tied to the incorporation of AI in rehabilitation and movement science courses. While acknowledging its potential to enhance learning experiences, it critically addresses the imperative to tackle ethical and pedagogical challenges. The paper offers guidance to strike a balance between educational innovation and academic responsibility. It emphasizes the need for a conscientious and planned implementation of AI, ensuring both scientific integrity and ethical adherence in this dynamically evolving field.

Quality of information about urologic pathology in English and Spanish from ChatGPT, BARD, and Copilot.

INTRODUCTION AND OBJECTIVE: Generative artificial intelligence makes it possible to ask about medical pathologies in dialog boxes. Our objective was to analyze the quality of information about the most common urological pathologies provided by ChatGPT (OpenIA), BARD (Google), and Copilot (Microsoft). METHODS: We analyzed information on the following pathologies and their treatments as provided by AI: prostate cancer, kidney cancer, bladder cancer, urinary lithiasis, and benign prostatic hypertrophy (BPH). Questions in English and Spanish were posed in dialog boxes; the answers were collected and analyzed with DISCERN questionnaires and the overall appropriateness of the response. Surgical procedures were performed with an informed consent questionnaire. RESULTS: The responses from the three chatbots explained the pathology, detailed risk factors, and described treatments. The difference is that BARD and Copilot provide external information citations, which ChatGPT does not. The highest DISCERN scores, in absolute numbers, were obtained in Copilot; however, on the appropriacy scale it was noted that their responses were not the most appropriate. The best surgical treatment scores were obtained by BARD, followed by ChatGPT, and finally Copilot. CONCLUSIONS: The answers obtained from generative AI on urological diseases depended on the formulation of the question. The information provided had significant biases, depending on pathology, language, and above all, the dialog box consulted.

Una revisión sistemática del uso de la inteligencia artificial en la educación / A systematic review of the use of artificial intelligence in education

Introducción. El uso de la inteligencia artificial (IA) en la educación ha sido objeto de una creciente atención en los últimos años. La IA se ha utilizado para mejorar la personalización del aprendizaje, la retroalimentación y la evaluación de los estudiantes. Sin embargo, también hay desafíos y limitaciones asociados. El objetivo de este trabajo fue identificar las principales tendencias y áreas de aplicación de la inteligencia artificial en la educación, así como analizar los beneficios y limitaciones de su uso en este ámbito. Métodos. Se llevó a cabo una revisión sistemática que exploró el empleo de la inteligencia artificial en el ámbito educativo. Esta revisión siguió una metodología de investigación basada en la búsqueda de literatura, compuesta por cinco etapas. La investigación se realizó utilizando Scopus como fuente de consulta primaria y se empleó la herramienta VOSviewer para analizar los resultados obtenidos. Resultados. Se encontraron numerosos estudios que investigan el uso de la IA en la educación. Los resultados sugieren que la IA puede mejorar significativamente la personalización del aprendizaje, proporcionando recomendaciones de actividades y retroalimentación adaptadas a las necesidades individuales de cada estudiante. Conclusiones. A pesar de las ventajas del uso de la IA en la educación, también hay desafíos y limitaciones que deben abordarse, como la calidad de los datos utilizados por la IA, la necesidad de capacitación para educadores y estudiantes, y las preocupaciones sobre la privacidad y la seguridad de los datos de los estudiantes. Es importante seguir evaluando los efectos del uso de la IA en la educación para garantizar su uso efectivo y responsable.

Introduction. The use of artificial intelligence (AI) in education has been the subject of increasing attention in recent years. AI has been used to improve personalized learning, feedback, and student assessment. However, there are also challenges and limitations. The aim of this study was to identify the main trends and areas of application of artificial intelligence in education, as well as to analyze the benefits and limitations of its use in this field. Methods. A systematic review was carried out on the use of artificial intelligence in education, using a literature search research methodology with five stages, based on the Scopus query and the tool for analyzing results with VOSviewer. Results. Numerous studies investigating the use of AI in education were found. The results suggest that AI can significantly improve personalized learning by providing activity recommendations and feedback tailored to the individual needs of each student. Conclusions. Despite the advantages of using AI in education, there are also challenges and limitations that need to be addressed, such as the quality of data used by AI, the need for training for educators and students, and concerns about the privacy and security of student data. It is important to continue evaluating the effects of AI use in education to ensure its effective and responsible use.

Continuous innovation in precision radio-guided surgery.

Since its origins, nuclear medicine has faced technological changes that led to modifying operating modes and adapting protocols. In the field of radioguided surgery, the incorporation of preoperative scintigraphic imaging and intraoperative detection with the gamma probe provided a definitive boost to sentinel lymph node biopsy to become a standard procedure for melanoma and breast cancer. The various technological innovations and consequent adaptation of protocols come together in the coexistence of the disruptive and the gradual. As obvious examples we have the introduction of SPECT/CT in the preoperative field and Drop-in probes in the intraoperative field. Other innovative aspects with possible application in radio-guided surgery are based on the application of artificial intelligence, navigation and telecare.

Artificial intelligence to predict bed bath time in Intensive Care Units / Inteligencia artificial para predecir la hora del baño en cama en Unidades de Cuidados Intensivos / Inteligência artificial para predição do tempo de banho no leito em Unidades de Terapia Intensiva

ABSTRACT Objectives: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients. Methods: a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed. Results: among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%. Conclusions: the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.

RESUMEN Objetivos: evaluar el rendimiento predictivo de diferentes algoritmos de inteligencia artificial para estimar el tiempo de ejecución del baño en cama en pacientes críticos. Métodos: estudio metodológico, que utilizó algoritmos de inteligencia artificial para predecir el tiempo de baño en cama en pacientes críticos. Se analizaron los resultados de modelos de regresión múltiple, redes neuronales perceptrón multicapa y función de base radial, árbol de decisión y random forest. Resultados: entre los modelos evaluados, el modelo de red neuronal con función de base radial, que contiene 13 neuronas en la capa oculta, presentó el mejor desempeño predictivo para estimar el tiempo de ejecución del baño en cama. En la validación de datos, la correlación al cuadrado entre los valores predichos y los valores originales fue del 62,3%. Conclusiones: el modelo de red neuronal con función de base radial mostró mejor rendimiento predictivo para estimar el tiempo de ejecución del baño en cama en pacientes críticos.

RESUMO Objetivos: avaliar a performance preditiva de diferentes algoritmos de inteligência artificial para estimar o tempo de execução do banho no leito em pacientes críticos. Métodos: estudo metodológico, que utilizou algoritmos de inteligência artificial para predizer o tempo de banho no leito em pacientes críticos. Foram analisados os resultados dos modelos de regressão múltipla, redes neurais perceptron multicamadas e função de base radial, árvore de decisão e random forest. Resultados: entre os modelos avaliados, o modelo de rede neural com função de base radial, contendo 13 neurônios na camada oculta, apresentou melhor performance preditiva para estimar o tempo de execução do banho no leito. Na validação dos dados, o quadrado da correlação entre os valores preditos e os valores originais foi de 62,3%. Conclusões: o modelo de rede neural com função de base radial apresentou melhor performance preditiva para estimar o tempo de execução do banho no leito em pacientes críticos.

Diagnóstico da retinopatia diabética por inteligência artificial por meio de smartphone / Diagnosis of diabetic retinopathy by artificial intelligence using smartphone

RESUMO Objetivo: Obter imagens de fundoscopia por meio de equipamento portátil e de baixo custo e, usando inteligência artificial, avaliar a presença de retinopatia diabética. Métodos: Por meio de um smartphone acoplado a um dispositivo com lente de 20D, foram obtidas imagens de fundo de olhos de pacientes diabéticos; usando a inteligência artificial, a presença de retinopatia diabética foi classificada por algoritmo binário. Resultados: Foram avaliadas 97 imagens da fundoscopia ocular (45 normais e 52 com retinopatia diabética). Com auxílio da inteligência artificial, houve acurácia diagnóstica em torno de 70 a 100% na classificação da presença de retinopatia diabética. Conclusão: A abordagem usando dispositivo portátil de baixo custo apresentou eficácia satisfatória na triagem de pacientes diabéticos com ou sem retinopatia diabética, sendo útil para locais sem condições de infraestrutura.

ABSTRACT Introduction: To obtain fundoscopy images through portable and low-cost equipment using artificial intelligence to assess the presence of DR. Methods: Fundus images of diabetic patients' eyes were obtained by using a smartphone coupled to a device with a 20D lens. By using artificial intelligence (AI), the presence of DR was classified by a binary algorithm. Results: 97 ocular fundoscopy images were evaluated (45 normal and 52 with DR). Through AI diagnostic accuracy around was 70% to 100% in the classification of the presence of DR. Conclusion: The approach using a low-cost portable device showed satisfactory efficacy in the screening of diabetic patients with or without diabetic retinopathy, being useful for places without infrastructure conditions.

A literacia em saúde no ChatGPT: explorando o potencial de uso de inteligência artificial para a elaboração de textos acadêmicos / Health literacy in ChatGPT: exploring the potential of the use of artificial intelligence to produce academic text

Resumo O presente estudo buscou conhecer as principais características das respostas geradas pela ferramenta ChatGPT a consultas sobre um tema emergente na literatura acadêmica de língua portuguesa - a literacia em saúde -, assim como discutir de que forma tais evidências podem contribuir para uma melhor compreensão sobre os limites e os desafios relacionados ao uso de Inteligência Artificial (IA) para a construção do conhecimento acadêmico. Trata-se de um estudo descritivo e exploratório, baseado em consultas ao ChatGPT, a partir de cinco perguntas disparadoras, feitas em sequência, nas línguas portuguesa (Brasil) e inglesa, com níveis de complexidade linguística crescentes. A análise dos resultados evidenciou uma ampla perspectiva para o uso de tecnologias baseadas em IA, como o ChatGPT, uma ferramenta disponibilizada de forma ampla e irrestrita, com uma interface intuitiva e simples, que se mostrou capaz de gerar textos coerentes, estruturados, em linguagem natural. Considerando o fenômeno do produtivismo acadêmico, associado a uma tendência crescente de má conduta profissional, sobretudo o plágio, coloca-se necessidade de um olhar ainda mais cuidadoso sobre o processo de produção e divulgação do conhecimento científico mediado por tecnologias de IA.

Abstract The aim of this study was to identify and analyze the main constituent elements of text generated by ChatGPT in response to questions on an emerging topic in the academic literature in Portuguese - health literacy - and discuss how the evidence produced can contribute to improving our understanding of the limits and challenges of using artificial intelligence (AI) in academic writing. We conducted an exploratory descriptive study based on responses to five consecutive questions in Portuguese and English with increasing levels of complexity put to ChatGPT. Our findings reveal the potential of the use of widely available, unrestricted access AI-based technologies like ChatGPT for academic writing. Featuring a simple and intuitive interface, the tool generated structured and coherent text using natural-like language. Considering that academic productivism is associated with a growing trend in professional misconduct, especially plagiarism, there is a need too take a careful look at academic writing and scientific knowledge dissemination processes mediated by AI technologies.

Ai based motion analysis software for sport and physical therapy assessment / Software de análisis de movimiento basado en ia para la evaluación del deporte y la fisioterapia / Software de análise de movimento baseado em ia para avaliação de esporte e fisioterapia

ABSTRACT Introduction: As the World Health Organization declared the novel coronavirus as a pandemic in March 2020, physical therapy is more difficult to execute, and social distancing is mandatory in the healthcare sector. Objective: In physical therapy, an online video analysis software that provides real-time graphic and numerical information about the patient's movement executions without direct personal contact would mean a significant improvement in eHealth treatment. Methods: We have developed a software layer on top of OpenPose human body position estimation software that can extract the time series of angles of arbitrary body parts using the output coordinates from OpenPose processing the data recorded by two cameras simultaneously. To validate the procedure of determining the joint angles using the Openpose software we have used the Kinovea software. Results: The comparison of the determined maximal knee angle in our and the Kinovea software, which is widely used in biomechanical measurements, was not significantly different (2.03±1.06°, p<0.05) Conclusion: This indicates, that the developed software can calculate the appropriate joint angles with the accuracy that physiotherapy treatments require. As, to our knowledge no such software yet exists, with the help of this software development, therapists could control and correct the exercises in real-time, and also from a distance, and physical therapy effectiveness could be increased. Level of Evidence II; Experimental, comparative.

RESUMEN Introducción: Como la Organización Mundial de la Salud declaró el nuevo coronavirus como una pandemia en marzo de 2020, la fisioterapia es más difícil de ejecutar, el distanciamiento social es obligatorio en el sector de la salud. Objetivo: En la práctica de fisioterapia un software de análisis de vídeo online que proporcione información gráfica y numérica en tiempo real sobre las ejecuciones de movimiento del paciente sin contacto personal directo supondría una mejora significativa en el tratamiento de la eSalud. Métodos: Fue desarrollado una capa de software sobre el software de estimación de posición del cuerpo humano OpenPose que puede extraer la serie temporal de ángulos de partes arbitrarias del cuerpo utilizando las coordenadas de salida de OpenPose procesando los datos registrados por dos cámaras simultáneamente. Para validar el procedimiento de determinación de los ángulos articulares mediante el software Openpose fue utilizado el software Kinovea. Resultados: La comparación del ángulo máximo de rodilla determinado en nuestro software y Kinovea, que es ampliamente utilizado en mediciones biomecánicas, no fue significativamente diferente (2,03±1,06°, p<0,05). Conclusión: Esto indica que el software desarrollado puede calcular los ángulos articulares adecuados con la precisión que requieren los tratamientos de fisioterapia. Dado que aún no existe dicho software, con la ayuda de este desarrollo de software, los terapeutas podrían controlar y corregir los ejercicios en tiempo real, y también a distancia, y se podría aumentar la eficacia de la fisioterapia. Nivel de Evidencia II; Experimental, comparativo.

RESUMO Introdução: Como a Organização Mundial da Saúde declarou o novo coronavírus como pandemia em março de 2020, a fisioterapia é mais difícil de executar, o distanciamento social é obrigatório no setor de saúde. Objetivo: Na prática da fisioterapia, um software de análise de vídeo online que fornece informações gráficas e numéricas em tempo real sobre as execuções de movimento do paciente sem contato pessoal direto significaria uma melhora significativa no tratamento eHealth. Métodos: Desenvolveu-se uma camada de software em cima do software de estimativa de posição do corpo humano OpenPose que pode extrair as séries temporais de ângulos de partes do corpo arbitrárias usando as coordenadas de saída do OpenPose processando os dados gravados por duas câmeras simultaneamente. Para validar o procedimento de determinação dos ângulos articulares utilizando o software Openpose utilizou-se o software Kinovea. Resultados: A comparação do ângulo máximo do joelho determinado em nosso e no software Kinovea, amplamente utilizado em medidas biomecânicas, não foi significativamente diferente (2,03±1,06°, p<0,05) Conclusão: Isso indica que o software desenvolvido pode calcular os ângulos articulares adequados com a precisão que os tratamentos de fisioterapia exigem. Como esse software ainda não existe, com a ajuda do desenvolvimento desse software, os terapeutas puderam controlar e corrigir os exercícios em tempo real, e também à distância, aumentando a eficácia da fisioterapia. Nível de Evidência II; Experimental, comparativo.

DATA GOVERNANCE in digital surgery.

Technological and computer advances have led to a "new era" of Surgery called Digital Surgery. In it, the management of information is the key. The development of Artificial Intelligence requires "Big Data" to create its algorithms. The use of digital technology for the systematic capture of data from the surgical process raises ethical issues of privacy, property, and consent. The use of these out-of-control data creates uncertainty and can be a source of mistrust and refusal by surgeons to allow its use, requiring a framework for the correct management of them. This paper exposes the current situation of Data Governance in Digital Surgery, the challenges posed and the lines of action necessary to resolve the areas of uncertainty that have arisen in the process, in which the surgeon must play a relevant role.

Impacto de la inteligencia artificial en las métricas terapéuticas del ataque cerebrovascular durante la pandemia COVID-19 / Impact of artificial intelligence on therapeutic metrics of cerebrovascular attack during the COVID-19 pandemic

Resumen Introducción : El inicio de la pandemia COVID-19, obligó a implementar cambios en el sistema de aten ción de los servicios de emergencia. Coincidentemente, en nuestra institución, implementamos el software de inteligencia artificial (IA), RAPID.AI, para el análisis de imágenes en el ataque cerebrovascular isquémico (ACVi). Nuestro objetivo fue evaluar el impacto del uso de la IA junto a los cambios en el triage durante la pandemia por COVID-19 en pacientes con ACVi por oclusión de gran vaso cerebral (OGVC). Métodos : Se crearon 2 grupos de pacientes con ACVi por OGVC tratados con terapia de reperfusión endovenosa más endovascular o terapia endovascu lar directa. Grupo 1: pacientes de enero 2019 a junio 2020; Grupo 2: pacientes de julio 2020 a diciembre de 2021, estudiados con RAPID.AI. Se analizaron datos clínicos, y métricas temporales. Se compararon según hora de arribo de 08:00 a 20:00 h (diurno) vs. 20:01 a 7:59 h (nocturno). Resultados : El grupo 1 comprendió 153 pacientes y el grupo 2 133. En el grupo 2 la métrica puerta-imagen y adquisición de la imagen fueron menores, con menor tiempo puerta-inicio de imagen y puerta-recanalización; los pacientes en horario nocturno presentaron mayor NIHSS y tiempos inicio-ingreso con menor proporción de independencia funcional a 90 días. Conclusiones : El uso de la IA para el análisis de imá genes junto a un menor tiempo puerta-fin de imagen, permitió acortar el intervalo hasta la punción inguinal. En el análisis por horarios durante la pandemia, los pacientes ingresados en horario diurno presentaron métricas puerta-imagen, tiempo de imagen y puerta-recanalización significativamente menores.

Abstract Introduction : The start of the COVID-19 pandemic forced the implementation of changes in the emergency services care system. Concomitantly, at our institution, we implemented the artificial intelligence (AI) software, RAPID.AI, for image analysis in ischemic stroke (IS). Our objective was to evaluate the impact of the use of AI together with the changes in the triage during the COVID-19 pandemic in patients with stroke due to large vessel occlusion (LVO). Methods : We included patients with IS due to LVO treated with intravenous reperfusion therapy plus en dovascular or direct endovascular therapy. Results : Two groups were created. Group 1: patients from January 2019 to June 2020; Group 2: patients from July 2020 to December 2021, studied with RAPID.AI. Clini cal data and temporal metrics were analyzed. They were compared according to arrival time from 08:00 to 20:00 (daytime) vs 20:01 to 7:59 (night). Results: We included 286 patients, 153 in group 1 and 133 in group 2. In group 2, door-image metric and image duration were lower, with shorter door-image onset and door-recanalization times; patients who arrived at night had higher NIHSS and longer time from onset-to-door with lower propor tion of functional independence at 90 days (mRS ≤ 2). Conclusions : The use of AI for image analysis along with a shorter door to end of image time allowed to reduce the interval to groin puncture. In the analysis by hours during the pandemic, patients admitted in daytime hours had significantly lower door to image, image time acquisition, and door to recanalization metrics.

Neuroculture and aesthetics: exploring the multidisciplinary ambiance of art, beauty and the human brain / Neurocultura e estética: explorando o ambiente multidisciplinar da arte, da beleza e do cérebro humano

This narrative review addresses the intersection between neuroculture and aesthetics, exploring the intricate relationship between neuroscience and the perception of beauty. The presentation begins by mentioning the philosophical foundations of aesthetics and moves on to the neural basis behind the sensory and emotional processing of beauty. Progressing further, it presents the intricate networks involved in interactions and responses to art, elucidating the brain's mechanisms for appreciating artistic stimuli. Finally, it investigates the neural networks associated with deriving personal and symbolic meaning from art forms, shedding light on how our brains deduce meaning and value from aesthetic experiences. Thus, there is an integration of studies based on the connectome with neuroaesthetics, on how the complete network of neural connections in the brain influences and shapes the way we perceive, interpret, and appreciate beauty. Furthermore, the article addresses the impact of virtual reality and artificial intelligence on traditional concepts of creativity, challenging existing paradigms. Concluding, it explores the potential educational and therapeutic applications of 'Visual Thinking Strategies' in promoting artistic engagement with potential educational and therapeutic applications.

Esta revisão narrativa aborda a intersecção entre neurocultura e estética, explorando a intrincada relação entre neurociência e a percepção da beleza. A apresentação começa mencionando os fundamentos filosóficos da estética e avança para a base neural por trás do processamento sensorial e emocional da beleza. Progredindo ainda mais, apresenta as intrincadas redes envolvidas nas interações e respostas à arte, elucidando os mecanismos do cérebro para apreciar estímulos artísticos. Por fim, investiga as redes neurais associadas à obtenção de significado pessoal e simbólico das formas de arte, esclarecendo como nossos cérebros deduzem significado e valor de experiências estéticas. Assim, há uma integração de estudos baseados no conectoma com a neuroestética, sobre como a rede completa de conexões neurais cerebrais influencia e molda a maneira como percebemos, interpretamos e apreciamos a beleza. Além disso, o artigo aborda o impacto da realidade virtual e da inteligência artificial nos conceitos tradicionais de criatividade, desafiando os paradigmas existentes. Concluindo, explora as potenciais aplicações educativas e terapêuticas das 'Estratégias de Pensamento Visual' na promoção do envolvimento artístico com potenciais aplicações educativas e terapêuticas.

Design and validation of an artificial intelligence system to detect the quality of colon cleansing before colonoscopy.

BACKGROUND AND AIMS: Patients' perception of their bowel cleansing quality may guide rescue cleansing strategies before colonoscopy. The main aim of this study was to train and validate a convolutional neural network (CNN) for classifying rectal effluent during bowel preparation intake as "adequate" or "inadequate" cleansing before colonoscopy. PATIENTS AND METHODS: Patients referred for outpatient colonoscopy were asked to provide images of their rectal effluent during the bowel preparation process. The images were categorized as adequate or inadequate cleansing based on a predefined 4-picture quality scale. A total of 1203 images were collected from 660 patients. The initial dataset (799 images), was split into a training set (80%) and a validation set (20%). The second dataset (404 images) was used to develop a second test of the CNN accuracy. Afterward, CNN prediction was prospectively compared with the Boston Bowel Preparation Scale (BBPS) in 200 additional patients who provided a picture of their last rectal effluent. RESULTS: On the initial dataset, a global accuracy of 97.49%, a sensitivity of 98.17% and a specificity of 96.66% were obtained using the CNN model. On the second dataset, an accuracy of 95%, a sensitivity of 99.60% and a specificity of 87.41% were obtained. The results from the CNN model were significantly associated with those from the BBPS (P<0.001), and 77.78% of the patients with poor bowel preparation were correctly classified. CONCLUSION: The designed CNN is capable of classifying "adequate cleansing" and "inadequate cleansing" images with high accuracy.

Consejos para escribir un artículo científico. Parte 1: la práctica de la escritura / Tips for Writing a Scientific Paper: The Practice of Writing

La escritura de artículos académicos es una competencia necesaria para la difusión del conocimiento científico y para el desarrollo profesional de quienes trabajan en diversas disciplinas. Sin embargo, a pesar de su importancia, esta habilidad compleja no suele ser enseñada en forma sistemática, lo que puede operar como una barrera para que los investigadores comuniquen los resultados de sus trabajos. En esta primera entrega, sintetizamos los principales consejos que han brindado expertos en la temática, añadiendo algunos de nuestra experiencia personal que consideramos útiles para facilitar el proceso de la escritura académica y el desarrollo de esta competencia en un contexto colaborativo. En una segunda entrega profundizaremos respecto de la problemática de la escritura de las diferentes secciones de un artículo científico y se ofrecerán consejos para optimizarla y volverla lo más eficaz posible. (AU)

Academic writing is essential for scientific knowledge dissemination and the professional development of those working in various disciplines. Yet, however important this complex skill is, it is not usually taught systematically, a fact that can act as a barrier for researchers to communicate the results of their work. In this first part, we synthesize the main tips provided by experts in the field, adding some of our personal experiences that they consider relevant to facilitate the process of academic writing and develop this skill in a collaborative context. In a second article, we will go deeper into the problem of writing the different sections of a scientific article and offer advice on ways to optimize it and make it as effective as possible. (AU)

ChatGPT: oportunidades y riesgos en la asistencia, docencia e investigación médica / ChatGPT: opportunities and risks in the fields of medical care, teaching, and research

Resumen ChatGPT es un asistente virtual con inteligencia artificial que utiliza lenguaje natural para comunicarse, es decir, mantiene conversaciones como las que se tendrían con otro humano. Puede aplicarse en educación a todos los niveles, que incluye la educación médica, tanto para la formación, la investigación, la escritura de artículos científicos, la atención clínica y la medicina personalizada. Puede modificar la interacción entre médicos y pacientes para mejorar los estándares de calidad de la atención médica y la seguridad, por ejemplo, al sugerir medidas preventivas en un paciente que en ocasiones no son consideradas por el médico por múltiples causas. Los usos potenciales del ChatGPT en la educación médica, como una herramienta de ayuda en la redacción de artículos científicos, un asistente en la atención para pacientes y médicos para una práctica más personalizada, son algunas de las aplicaciones que se analizan en este artículo. Los aspectos éticos, originalidad, contenido inapropiado o incorrecto, citas incorrectas, ciberseguridad, alucinaciones y plagio son ejemplos de las situaciones a tomar en cuenta al usar las herramientas basadas en inteligencia artificial en medicina.

Abstract ChatGPT is a virtual assistant with artificial intelligence (AI) that uses natural language to communicate, i.e., it holds conversations as those that would take place with another human being. It can be applied at all educational levels, including medical education, where it can impact medical training, research, the writing of scientific articles, clinical care, and personalized medicine. It can modify interactions between physicians and patients and thus improve the standards of healthcare quality and safety, for example, by suggesting preventive measures in a patient that sometimes are not considered by the physician for multiple reasons. ChatGPT potential uses in medical education, as a tool to support the writing of scientific articles, as a medical care assistant for patients and doctors for a more personalized medical approach, are some of the applications discussed in this article. Ethical aspects, originality, inappropriate or incorrect content, incorrect citations, cybersecurity, hallucinations, and plagiarism are some examples of situations to be considered when using AI-based tools in medicine.

Chatbots, generative AI, and scholarly manuscripts: WAME recommendations on chatbots and generative artificial intelligence in relation to scholarly publications

This statement revises our earlier "WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications" (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work and to address the need for all journal editors to have access to manuscript screening tools. In this rapidly evolving field, we will continue to modify these recommendations as the software and its applications develop.

Esta declaración revisa las anteriores "Recomendaciones de WAME sobre ChatGPT y Chatbots en Relation to Scholarly Publications" (20 de enero de 2023). La revisión refleja la proliferación de chatbots y su creciente uso en las publicaciones académicas en los últimos meses, así como la preocupación por la falta de autenticidad de los contenidos cuando se utilizan chatbots. Estas recomendaciones pretenden informar a los editores y ayudarles a desarrollar políticas para el uso de chatbots en los artículos sometidos en sus revistas. Su objetivo es ayudar a autores y revisores a entender cuál es la mejor manera de atribuir el uso de chatbots en su trabajo y a la necesidad de que todos los editores de revistas tengan acceso a herramientas de selección de manuscritos. En este campo en rápida evolución, seguiremos modificando estas recomendaciones a medida que se desarrollen el software y sus aplicaciones.

Bioética e integración de la inteligencia artificial en la medicina: reflexiones desde el cine sobre la condición humana y la responsabilidad hacia las generaciones futuras / Bioethics and Integration of AI in Medicine: reflections from Cinema about the Human Condition and Responsibility towards Future Generations

La discusión sobre la ética en torno a la inteligencia artificial y la medicina ha ganado cada vez más relevancia en el ámbito académico y público. Independientemente de los diversos enfoques, hay un hecho innegable: la práctica médica y todos los agentes involucrados, tanto profesionales como usuarios, se verán condicionados por la inteligencia artificial. En este análisis ético narrativo, basado en el cine, se aborda la condición humana y la responsabilidad hacia las generaciones futuras como elementos cruciales dentro de la discusión bioética y fundamentales para lograr una incorporación reflexiva y coherente de la inteligencia artificial en la medicina. Como conclusión, se propone que la autenticidad, la responsabilidad y el diálogo son pilares esenciales en el proceso de integración de esta tecnología

The discussion on the ethics surrounding artificial intelligence and medicine has gained increasing relevance in the academic and public sphere. Regardless of the various approaches, there is an undeniable fact: medical practice and all the agents involved, both professionals and users, will be conditioned by artificial intelligence. In this narrative ethical analysis, based on cinema, the human condition and responsibility towards future generations are addressed as crucial elements within the bioethical discussion and fundamental to achieve a thoughtful and coherent incorporation of artificial intelligence in medicine. In conclusion, it is proposed that authenticity, responsibility and dialogue are essential pillars in the process of integration of this technology