Teacher Emotion Inventory: validación en una muestra de profesorado español / Teacher Emotion Inventory: validation in a sample of Spanish teachers

Antecedentes: La escala Teacher Emotion Inventory (TEI) es un instrumento que evalúa emociones discretas experimentadas por el profesorado en el proceso de enseñanza-aprendizaje. El objetivo de este estudio es examinar las propiedades psicométricas de la versión breve española de la escala Teacher Emotion Inventory (TEI-BSV) en una muestra de 567 profesores (65.5% son mujeres), con edades comprendidas entre 25 y 65 años (M = 46.04; DT = 9.09). Método: Tras su adaptación mediante traducción inversa, el profesorado completó una batería que incluía el TEI-BSV, un cuestionario de inteligencia emocional, dos escalas de bienestar subjetivo, una escala sobre burnout y una escala sobre engagement. Resultados: Los resultados mostraron una consistencia interna adecuada de las subescalas del TEI-BSV. Los análisis factoriales (exploratorio y confirmatorio) proporcionaron pruebas de que el TEI-BSV tiene una estructura de cuatro factores con un buen ajuste, frente a la estructura de cinco factores original. Se han hallado evidencias de validez convergente, así como de validez criterial e incremental del TEI-BSV. Conclusiones: el TEI-BSV podría ser una herramienta útil para la evaluación ecológica de las emociones discretas del profesorado en su contexto laboral.(AU)

Background: The Teacher Emotion Inventory (TEI) scale is an instrument that evaluates discrete emotions experienced by teachers in the teaching-learning process. The aim of this study was to examine the psychometric properties of the brief Spanish version of the Teacher Emotion Inventory scale (TEI-BSV) using a sample of 567 teachers (65.5% women), aged between 25 and 65 years (M= 46.04; SD= 9.09). Methods: After adaptation through back-translation, the teachers com-pleted a battery of tests included in the TEI-BSV: an emotional intelli-gence questionnaire, two subjective well-being scales, a burnout scale and a scale on engagement. Results: The data revealed adequate internal consistency of the TEI-BSV subscales, and exploratory and confirma-tory factor analyses provided evidence that the TEI-BSV has a four-factor structure with good adjustment, as opposed to the original five-factor structure proposed. There was evidence of convergent validity of the TEI-BSV, as well as criterion and incremental validity. Conclusions: The TEI-BSV could be a useful instrument for the ecological assess-ment of teachers' discrete emotions in the context of their workplace.(AU)

Artificial intelligence-assisted identification and retrieval of chronic hepatitis C patients lost to follow-up in the health area of Pontevedra and O Salnés (Spain).

OBJECTIVE: Direct-acting antivirals (DAAs) to treat hepatitis C virus (HCV) infection offer an opportunity to eliminate the disease. This study aimed to identify and relink to care HCV patients previously lost to medical follow-up in the health area of Pontevedra and O Salnés (Spain) using an artificial intelligence-assisted system. PATIENTS AND METHODS: Active retrospective search of previously diagnosed HCV cases recorded in the Galician Health Service proprietary health information exchange database using the Herramientas para la EXplotación de la INformación (HEXIN) application. RESULTS AND CONCLUSIONS: Out of 99 lost patients identified, 64 (64.6%) were retrieved. Of these, 62 (96.88%) initiated DAA treatment and 54 patients (87.1%) achieved a sustained virological response. Mean time from HCV diagnosis was over 10 years. Main reasons for loss to follow-up were fear of possible adverse effects of treatment (30%) and mobility impediments (21%). Among the retrieved patients, almost one in three presented advanced liver fibrosis (F3) or cirrhosis (F4) at evaluation. In sum, HCV patients lost to follow-up can be retrieved by screening past laboratory records. This strategy promotes the achievement of HCV elimination goals.

Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology.

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.

ChatGPT es un estudiante por encima de la media en la Facultad de Medicina de la Universidad de Zaragoza y un colaborador excelente en la elaboración de material docente / ChatGPT is an above-average student at the Faculty of Medicine of the University of Zaragoza and an excellent collaborator in the development of teaching materials

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)

Progress in medicine and artificial intelligence.

This essay questions, with regard to medicine, the idea of progress as technological development by focusing on people rather than things. It analyzes how the predominance of such an idea of progress converts today's societies to techno-fetishism that degrades community life and medical practice, contributing to the medicalization of social life. It is argued that the realization of technological potentialities depends on their forms of use; that the main motive of technological development is unlimited profit and that priority developments are those that enhance the social control that maintains the status quo. The intelligence as an intelligence quotient is criticized by proposing it as an attribute of the human being as a whole, manifested in the ways of thinking and proceeding of people in their circumstances, where affectivity and critical thinking are essential for their development; it is emphasized that its antecedent is the harmonic concert of planetary life that contrasts with the prevailing human disharmony. It is proposed that artificial intelligence is the most recent creation of techno-fetishism that deposits vital attributes in technology and that its forms of use will accentuate the degradation of human and planetary life. Another idea of medical progress is proposed, based on forms of organization conducive to the development of inquisitive, critical and collaborative skills that promote permanent improvement, whose distant horizon is dignifying progress: spiritual, intellectual, moral and convivial sublimation of collectivities in harmony with the planetary ecosystem.

Este ensayo cuestiona, a propósito de la medicina, la idea de progreso como desarrollo tecnológico al centrarlo en las personas y no en las cosas. Se analiza cómo el predominio de tal idea de progreso convierte a las sociedades actuales al tecno-fetichismo que degrada la vida comunitaria y la práctica médica contribuyendo a la medicalización de la vida social. Se argumenta que la realización de las potencialidades tecnológicas depende de sus formas de uso, que el móvil principal del desarrollo tecnológico es el lucro sin límites, y que los desarrollos prioritarios son los que potencian el control social que mantiene el statu quo. Se critica la idea de inteligencia como cociente intelectual al proponerla como atributo del ser humano como un todo, manifiesto en las formas pensar y proceder de las personas en sus circunstancias, donde la afectividad y el pensamiento crítico son imprescindibles para su desarrollo. Se destaca que su antecedente es el concierto armónico de la vida planetaria contrastante con la disarmonía humana imperante. Se plantea que la inteligencia artificial es la más reciente hechura del tecno-fetichismo que deposita en la tecnología atributos vitales, y que sus formas de uso acentuarán la degradación de la vida humana y planetaria. Se propone otra idea de progreso médico basado en formas de organización propicias para el desarrollo de aptitudes inquisitivas, críticas y colaborativas que impulsen la superación permanente, cuyo horizonte lejano es el progreso dignificante: sublimación espiritual, intelectual, moral y convivencial de las colectividades en armonía con el ecosistema planetario.

Progress in medicine and artificial intelligence.

This essay challenges the idea of progress as technological development in relation to medicine by focusing on people rather than things. It analyzes how the prevalence of such an idea of progress leads contemporary societies to a technofetishism that degrades community life and medical practice, contributing to the medicalization of social life. It is argued that the realization of technological potentialities depends on their forms of use, that the main motive of technological development is unlimited profit, and the priority developments are those that enhance social control which maintains the status quo. Intelligence as an intelligence quotient is criticized by proposing it as an attribute of the human being as a whole, manifested in the ways of thinking and acting of human beings in their circumstances, where affectivity and critical thinking are essential for their development; it is emphasized that its antecedent is the harmonic concert of planetary life, which contrasts with the prevailing human disharmony. It is proposed that artificial intelligence is the latest creation of technofetishism, which deposits vital attributes in technology, and that its use will accentuate the degradation of human and planetary life. Another idea of medical progress is proposed, based on forms of organization that is conducive to the development of inquisitive, critical, and collaborative skills that promote permanent improvement, whose distant horizon is dignified progress: the spiritual, intellectual, moral, and convivial sublimation of collectivities in harmony with the planetary ecosystem.

Este ensayo cuestiona, a propósito de la medicina, la idea de progreso como desarrollo tecnológico al centrarlo en las personas no en las cosas. Se analiza cómo el predominio de tal idea de progreso convierte a las sociedades actuales al tecno-fetichismo que degrada la vida comunitaria y la práctica médica contribuyendo a la medicalización de la vida social. Se argumenta: que la realización de las potencialidades tecnológicas depende de sus formas de uso; que el móvil principal del desarrollo tecnológico es el lucro sin límites y que los desarrollos prioritarios son los que potencian el control social que mantiene el statu quo. Se critica la idea de inteligencia como cociente intelectual al proponerla como atributo del ser humano como un todo, manifiesto en las formas pensar y proceder de las personas en sus circunstancias, donde la afectividad y el pensamiento crítico son imprescindibles para su desarrollo. Se destaca que su antecedente es el concierto armónico de la vida planetaria contrastante con la disarmonía humana imperante. Se plantea que la inteligencia artificial es la más reciente hechura del tecno-fetichismo que deposita en la tecnología atributos vitales y que sus formas de uso acentuarán la degradación de la vida humana y planetaria. Se propone otra idea de progreso médico basado en formas de organización propicias para el desarrollo de aptitudes inquisitivas, críticas y colaborativas que impulsen la superación permanente, cuyo horizonte lejano es el progreso dignificante: sublimación espiritual, intelectual, moral y convivencial de las colectividades en armonía con el ecosistema planetario.

The role of artificial intelligence in scientific publishing: Perspectives from Hospital Pharmacy. / El rol de la inteligencia artificial en la publicación científica: perspectivas desde la farmacia hospitalaria.

The article examines the impact of artificial intelligence on scientific writing, with a particular focus on its application in hospital pharmacy. It analyzes artificial intelligence tools that enhance information retrieval, literature analysis, writing quality, and manuscript drafting. Chatbots like Consensus, along with platforms such as Scite and SciSpace, enable precise searches in scientific databases, providing evidence-based responses and references. SciSpace facilitates the generation of comparative tables and the formulation of queries regarding studies, while ResearchRabbit maps the scientific literature to identify trends. Tools like DeepL and ProWritingAid improve writing quality by correcting grammatical, stylistic, and plagiarism errors. A.R.I.A. enhances reference management, and Jenny AI assists in overcoming writer's block. Python libraries such as LangChain enable advanced semantic searches and the creation of agents. Despite their benefits, artificial intelligence raises ethical concerns including biases, misinformation, and plagiarism. The importance of responsible use and critical review by experts is emphasized. In hospital pharmacy, artificial intelligence can enhance efficiency and precision in research and scientific communication. Pharmacists can use these tools to stay updated, enhance the quality of their publications, optimize information management, and facilitate clinical decision-making. In conclusion, artificial intelligence is a powerful tool for hospital pharmacy, provided it is used responsibly and ethically.

Oral anticoagulant treatment in atrial fibrillation: the AFIRMA real-world study using natural language processing and machine learning.

INTRODUCTION: Oral anticoagulation (OAC) is key in atrial fibrillation (AF) thromboprophylaxis, but Spain lacks substantial real-world evidence. We aimed to analyze the prevalence, clinical characteristics, and treatment patterns among patients with AF undertaking OAC, using natural language processing (NLP) and machine learning (ML). MATERIALS AND METHODS: This retrospective study included AF patients on OAC from 15 Spanish hospitals (2014-2020). Using EHRead® (including NLP and ML), and SNOMED_CT, we extracted and analyzed patient demographics, comorbidities, and OAC treatment from electronic health records. AF prevalence was estimated, and a descriptive analysis was conducted. RESULTS: Among 4,664,224 patients in our cohort, AF prevalence ranged from 1.9% to 2.9%. A total of 57,190 patients on OAC therapy were included, 80.7% receiving Vitamin K antagonists (VKA) and 19.3% Direct-acting OAC (DOAC). The median age was 78 and 76 years respectively, with males constituting 53% of the cohort. Comorbidities like hypertension (76.3%), diabetes (48.0%), heart failure (42.2%), and renal disease (18.7%) were common, and more frequent in VKA users. Over 50% had a high CHA2DS2-VASc score. The most frequent treatment switch was from DOAC to acenocoumarol (58.6% to 70.2%). In switches from VKA to DOAC, apixaban was the most chosen (35.2%). CONCLUSIONS: Utilizing NLP and ML to extract RWD, we established the most comprehensive Spanish cohort of AF patients with OAC to date. Analysis revealed a high AF prevalence, patient complexity, and a marked VKA preference over DOAC. Importantly, in VKA to DOAC transitions, apixaban was the favored option.

El principio ético de la intervención humana en la responsabilidad civil

Una de las mayores complejidades que se presentan respecto de la responsabilidad civil por daños causados por sistemas de inteligencia artificial viene dada por la dificultad de atribuir la conducta que causa daño a un sujeto particular. Frente a ello, este artículo expone la importancia del principio ético de la intervención humana para la responsabilidad civil, cuya función consiste en constituir la guía para la interpretación y aplicación de sus reglas en los casos en los que, como resultado de una acción u omisión emanada de una decisión, recomendación o predicción realizada por un sistema de inteligencia artificial, se causen daños a las personas.

One of the main challenges associated with regard to civil liability for damages resulting from artificial intelligence systems is the difficulty of attributing the behavior that led to harm to a specific individual. The aim of this article is to highlight the significance of the ethical principle of human intervention for civil liability. This principle serves as a guide for interpreting and applying rules when artificial intelligence systems cause harm to individuals due to actions, decisions, recommendations or predictions.

Uma das maiores complexidades que se apresentam a respeito da responsabilidade civil por danos causados por sistemas de inteligência artificial vem dada pela dificuldade de atribuir a conduta que causa dano a um sujeito particular. Frente a isso, este artigo expõe a importância do princípio ético da intervenção humana para a responsabilidade civil, cuja função consiste em constituir uma orientação para a interpretação e aplicação de suas regras nos casos em que, como resultado de uma ação ou omissão emanada de uma decisão, recomendação ou previsão realizada por um sistema de inteligência artificial, se cause danos às pessoas.

Uso de aplicativo de inteligência artificial como fonte de informação para resolução de provas de Odontologia / Use of an artificial intelligence application as a source of information for solving Dentistry exams

Introdução: O ChatGPT® é uma ferramenta pública desenvolvida pela OpenAI que utiliza a tecnologia do modelo de linguagem GPT. Este chatbot é capaz de atender a variadas solicitações de texto. Objetivo: avaliar se o ChatGPT® é capaz de ser a única fonte de informação para resolução de provas de Odontologia. Material e métodos: consiste em um estudo transversal quantitativo analítico. Para a coleta de dados, foi elaborada uma prova fictícia constituída por questões do ENADE e de outros concursos públicos. Os participantes responderam a prova em dois momentos: T1, sem o ChatGPT® e, após 15 dias (T2), utilizando-o. A amostra foi de 30 discentes de graduação em Odontologia, divididos igualmente entre 3 grupos: 1º ao 4º semestre, 5º ao 6º semestre e 7º ao 10º semestre. Para análise de dados foram aplicadas análises estatísticas descritiva e inferencial, por meio do software SPSS, com os testes de Wilcoxon e de McNemar. Resultados: revelaram uma eficácia notável do ChatGPT® na resolução de questões discursivas, com 83,3% de taxa de acerto, enquanto os discentes deram mais respostas incorretas ou incompletas. Porém, foram observadas limitações da base de dados do ChatGPT® quanto às questões objetivas. É crucial ressaltar que, apesar de resultados promissores, a aplicação do Chat levanta questões éticas e pedagógicas. Assim, a introdução do ChatGPT® na educação preocupa quanto à validade e equidade nas avaliações, destacando a importância de encontrar equilíbrio entre a inovação tecnológica e a preservação da integridade acadêmica

Introduction: ChatGPT® is a public tool developed by OpenAI that employs the language model technology of GPT. This chatbot is capable of addressing various text-based requests. Objective: To assess whether ChatGPT® can be the sole source of information for resolving Dentistry exams. Materials and Methods: This is an analytical quantitative cross-sectional study. For data collection, a fictitious exam was created, consisting of questions from the National Student Performance Exam (ENADE) and other public competitions. Participants answered the exam at two different times: T1, without ChatGPT®, and, after 15 days (T2), using it. The sample included 30 undergraduate Dentistry students, equally divided into three groups: 1st to 4th semester, 5th to 6th semester, and 7th to 10th semester. Descriptive and inferential statistical analyses were applied using SPSS software, including the Wilcoxon and McNemar tests. Results: They revealed a notable effectiveness of ChatGPT® in resolving essay questions, with an 83.3% accuracy rate, while students provided more incorrect or incomplete answers. However, limitations of the ChatGPT® database were observed regarding objective questions. It is crucial to emphasize that, despite promising results, the application of Chat raises ethical and pedagogical questions. Therefore, the introduction of ChatGPT® in education raises concerns about the validity and fairness of assessments, underscoring the importance of finding a balance between technological innovation and the preservation of academic integrity

Design and validation of an artificial intelligence system to detect the quality of colon cleansing before colonoscopy / Diseño y validación de un sistema de inteligencia artificial para detectar la calidad de la limpieza del colon previa a la realización de la colonoscopia

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 methodsPatients 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.ResultsOn 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.ConclusionThe designed CNN is capable of classifying “adequate cleansing” and “inadequate cleansing” images with high accuracy. (AU)

Antecedentes y objetivos La percepción de los pacientes sobre la calidad de su limpieza intestinal puede guiar las estrategias de limpieza de rescate antes de una colonoscopia. El objetivo principal de este estudio fue entrenar y validar una red neuronal convolucional (CNN) para clasificar el efluente rectal durante la preparación intestinal como «adecuado» o «inadecuado».Pacientes y métodosPacientes no seleccionados proporcionaron imágenes del efluente rectal durante el proceso de preparación intestinal. Las imágenes fueron categorizadas como una limpieza adecuada o inadecuada según una escala de calidad de 4 imágenes predefinida. Se recopilaron un total de 1.203 imágenes de 660 pacientes. El conjunto de datos inicial (799 imágenes) se dividió en un conjunto de entrenamiento (80%) y un conjunto de validación (20%). Un segundo conjunto de datos (404 imágenes) se utilizó para evaluar la precisión de la CNN. Posteriormente, la predicción de la CNN se comparó prospectivamente con la escala de preparación colónica de Boston (BBPS) en 200 pacientes que proporcionaron una imagen de su último efluente rectal.ResultadosEn el conjunto de datos inicial, la precisión global fue del 97,49%, la sensibilidad del 98,17% y la especificidad del 96,66%. En el segundo conjunto de datos, se obtuvo una precisión del 95%, una sensibilidad del 99,60% y una especificidad del 87,41%. Los resultados del modelo de CNN se asociaron significativamente con la escala de preparación colónica de Boston (p<0,001), y el 77,78% de los pacientes con una preparación intestinal deficiente fueron clasificados correctamente.ConclusiónLa CNN diseñada es capaz de clasificar imágenes de «limpieza adecuada» y «limpieza inadecuada» con alta precisión. (AU)

Exploring the potential of Artificial Intelligence in Traumatology: Conversational answers to specific questions. / Explorando el potencial de la inteligencia artificial en traumatología: respuestas conversacionales a preguntas específicas.

INTRODUCTION: Generative Artificial Intelligence is a technology that provides greater connectivity with people through conversational bots («chatbots¼). These bots can engage in dialogue using natural language indistinguishable from humans and are a potential source of information for patients.The aim of this study is to examine the performance of these bots in solving specific issues related to orthopedic surgery and traumatology using questions from the Spanish MIR exam between 2008 and 2023. MATERIAL AND METHODS: Three «chatbot¼ models (ChatGPT, Bard and Perplexity) were analyzed by answering 114 questions from the MIR. Their accuracy was compared, the readability of their responses was evaluated, and their dependence on logical reasoning and internal and external information was examined. The type of error was also evaluated in the failures. RESULTS: ChatGPT obtained 72.81% correct answers, followed by Perplexity (67.54%) and Bard (60.53%).Bard provides the most readable and comprehensive responses. The responses demonstrated logical reasoning and the use of internal information from the question prompts. In 16 questions (14%), all 3 applications failed simultaneously. Errors were identified, including logical and information failures. CONCLUSIONS: While conversational bots can be useful in resolving medical questions, caution is advised due to the possibility of errors. Currently, they should be considered as a developing tool, and human opinion should prevail over Generative Artificial Intelligence.

Artificial intelligence for dysplasia detection during surveillance colonoscopy in patients with ulcerative colitis: A cross-sectional, non-inferiority, diagnostic test comparison study.

BACKGROUND AND STUDY AIM: High-definition virtual chromoendoscopy, along with targeted biopsies, is recommended for dysplasia surveillance in ulcerative colitis patients at risk for colorectal cancer. Computer-aided detection (CADe) systems aim to improve colonic adenoma detection, however their efficacy in detecting polyps and adenomas in this context remains unclear. This study evaluates the CADe Discovery™ system's effectiveness in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer. PATIENTS AND METHODS: A prospective cross-sectional, non-inferiority, diagnostic test comparison study was conducted on ulcerative colitis patients undergoing colorectal cancer surveillance colonoscopy between January 2021 and April 2021. Patients underwent virtual chromoendoscopy (VCE) with iSCAN 1 and 3 with optical enhancement. One endoscopist, blinded to CADe Discovery™ system results, examined colon sections, while a second endoscopist concurrently reviewed CADe images. Suspicious areas detected by both techniques underwent resection. Proportions of dysplastic lesions and patients with dysplasia detected by VCE or CADe were calculated. RESULTS: Fifty-two patients were included, and 48 lesions analyzed. VCE and CADe each detected 9 cases of dysplasia (21.4% and 20.0%, respectively; p=0.629) in 8 patients and 7 patients (15.4% vs. 13.5%, respectively; p=0.713). Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy for dysplasia detection using VCE or CADe were 90% and 90%, 13% and 5%, 21% and 2%, 83% and 67%, and 29.2% and 22.9%, respectively. CONCLUSIONS: The CADe Discovery™ system shows similar diagnostic performance to VCE with iSCAN in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer.

Applications of artificial intelligence in surgery: clinical, technical, and governance considerations.

Artificial intelligence (AI) will power many of the tools in the armamentarium of digital surgeons. AI methods and surgical proof-of-concept flourish, but we have yet to witness clinical translation and value. Here we exemplify the potential of AI in the care pathway of colorectal cancer patients and discuss clinical, technical, and governance considerations of major importance for the safe translation of surgical AI for the benefit of our patients and practices.

Classification tree obtained by artificial intelligence for the prediction of heart failure after acute coronary syndromes. / Árboles de clasificación obtenidos mediante inteligencia artificial para la predicción de insuficiencia cardiaca tras el síndrome coronario agudo.

BACKGROUND: Coronary heart disease is the leading cause of heart failure (HF), and tools are needed to identify patients with a higher probability of developing HF after an acute coronary syndrome (ACS). Artificial intelligence (AI) has proven to be useful in identifying variables related to the development of cardiovascular complications. METHODS: We included all consecutive patients discharged after ACS in two Spanish centers between 2006 and 2017. Clinical data were collected and patients were followed up for a median of 53months. Decision tree models were created by the model-based recursive partitioning algorithm. RESULTS: The cohort consisted of 7,097 patients with a median follow-up of 53months (interquartile range: 18-77). The readmission rate for HF was 13.6% (964 patients). Eight relevant variables were identified to predict HF hospitalization time: HF at index hospitalization, diabetes, atrial fibrillation, glomerular filtration rate, age, Charlson index, hemoglobin, and left ventricular ejection fraction. The decision tree model provided 15 clinical risk patterns with significantly different HF readmission rates. CONCLUSIONS: The decision tree model, obtained by AI, identified 8 leading variables capable of predicting HF and generated 15 differentiated clinical patterns with respect to the probability of being hospitalized for HF. An electronic application was created and made available for free.

Competencia emocional y características sociodemográficas y profesionales de las enfermeras / Competência emocionais e características sociodemográficas e profissionais de enfermeiros / Emotional competence and sociodemographic and professional characteristics of nurses

Introducción: En los profesionales de la salud, las habilidades que les permitan lidiar con las emociones propias y ajenas garantizan la calidad de la atención brindada y una relación terapéutica eficaz. Por lo tanto, son fundamentales para los enfermeros, es decir, para aquellos que actúan en las unidades de salud de la familia. Objetivo: Analizar la relación entre la competencia emocional de las enfermeras que trabajan en unidades de salud de la familia en un grupo de centros de salud en el norte de Portugal y sus características sociodemográficas y profesionales. Método: Metodología cuantitativa, de tipo transversal descriptivo-correlacional. Datos recogidos a través de un cuestionario electrónico que constaba de dos partes: características sociodemográficas y profesionales de los participantes y cuestionario de competencia emocional. 66 enfermeras compusieron la muestra. Resultados: Las enfermeras del estudio mostraron altos niveles de competencia emocional (media = 205,1, desviación estándar = 20,9). No hubo diferencias estadísticamente significativas entre las características sociodemográficas y profesionales y la competencia emocional.Conclusiones: Aunque no está clara la relación entre la competencia emocional y las características sociodemográficas y profesionales, es cierta la importancia de la inteligencia emocional en la práctica asistencial. (AU)

Introdução: Em profissionais de saúde, competências que permitam lidar com as próprias emoções e com as dos outros garantem a qualidade dos cuidados prestados e uma relação terapêutica eficaz. Daí serem fundamentais para enfermeiros, nomeadamente para os que executem funções em unidades de saúde familiares. Objetivo: Analisar a relação entre a competência emocional dos enfermeiros das unidades de saúde familiar de um agrupamento de centros de saúde do norte de Portugal e as suas características sociodemográficas e profissionais. Método: Metodologia quantitativa, do tipo transversal descritivo-correlacional. Dados recolhidos através de um questionário eletrónico que consistia em duas partes: características sociodemográficas e profissionais dos participantes e questionário de competência emocional. 66 enfermeiros compuseram a amostra. Resultados: Os enfermeiros do estudo apresentaram elevados níveis de competência emocional (média = 205,1, desvio padrão = 20,9). Não se evidenciaram diferenças estatisticamente significativas entre as características sociodemográficas e profissionais e a competência emocional. Conclusões: Apesar de não ser clara a relação entre a competência emocional e as características sociodemográficas e profissionais, é certa a importância da inteligência emocional na prática de cuidados. (AU)

Introduction: In health professionals, skills that allow them to deal with their own emotions and those of others guarantee the quality of care provided and an effective therapeutic relationship. Hence, they are fundamental for nurses, namely for those who work in family health units. Objective: To analyze the relationship between the emotional competence of nurses working in family health units in a group of health centers in the north of Portugal and their sociodemographic and professional characteristics.Method: Quantitative methodology, of the transversal descriptive-correlational type. Data collected through an electronic questionnaire that consisted of two parts: sociodemographic and professional characteristics of the participants and emotional competence questionnaire. 66 nurses composed the sample.Results: The nurses in the study showed high levels of emotional competence (mean = 205.1, standard deviation = 20.9). There were no statistically significant differences between sociodemographic and professional characteristics and emotional competence. Conclusions: Although the relationship between emotional competence and sociodemographic and professional characteristics is unclear, the importance of emotional intelligence in care practice is certain. (AU)

La integración de la inteligencia artificial en la educación médica y su impacto en la práctica clínica / The integration of artificial intelligence in medical education and its impact on clinical practice

Introducción: La integración de la inteligencia artificial (IA) en la educación médica redefine paradigmas, optimiza méto-dos y forja una simbiosis tecnológica. Desarrollo: La IA potencia simulaciones clínicas, mejora evaluaciones y desarrolla habilidades blandas, redefiniendo lainteracción médico-paciente. Conclusiones: Aunque persisten desafíos éticos, la colaboración interdisciplinaria y la adaptabilidad son cruciales. La IA marca un hito en la evolución médica al elevar la calidad asistencial y establecer estándares para una colaboración armoniosa entre tecnología y compasión.(AU)

Introduction: The incorporation of artificial intelligence (AI) into medical education redefines paradigms, optimisesmethods and forges a technological symbiosis. Development: AI enhances clinical simulations, improves assessments and develops soft skills, thereby redefining doctor-patient interaction. Conclusions: Although ethical challenges remain, interdisciplinary collaboration and adaptability are crucial. AI marks a milestone in the evolution of medicine by raising the quality of care and setting standards for harmonious collaboration between technology and compassion.(AU)

Current perspectives on the use of artificial intelligence in critical patient safety.

Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligence (AI) emerges as a disruptive technology offering novel opportunities. While the published evidence is limited and presents methodological issues, certain areas show promise, such as decision support systems, detection of adverse events, and prescription error identification. The application of AI in safety may pursue predictive or diagnostic objectives. Implementing AI-based systems necessitates procedures to ensure secure assistance, addressing challenges including trust in such systems, biases, data quality, scalability, and ethical and confidentiality considerations. The development and application of AI demand thorough testing, encompassing retrospective data assessments, real-time validation with prospective cohorts, and efficacy demonstration in clinical trials. Algorithmic transparency and explainability are essential, with active involvement of clinical professionals being crucial in the implementation process.

[ChatGPT is an above-average student at the Faculty of Medicine of the University of Zaragoza and an excellent collaborator in the development of teaching materials]. / ChatGPT es un estudiante por encima de la media en la Facultad de Medicina de la Universidad de Zaragoza y un colaborador excelente en la elaboración de material docente.

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 Procedures¼ 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 «no¼, «false¼ or «incorrect¼ 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.