Comparing answers of artificial intelligence systems and clinical toxicologists to questions about poisoning: Can their answers be distinguished? / Comparación de las respuestas a preguntas sobre intoxicaciones generadas por sistemas de inteligencia artificial y las creadas por toxicólogos clínicos.

OBJECTIVE: To present questions about poisoning to 4 artificial intelligence (AI) systems and 4 clinical toxicologists and determine whether readers can identify the source of the answers. To evaluate and compare text quality and level of knowledge found in the AI and toxicologists' responses. METHODS: Ten questions about toxicology were presented to the following AI systems: Copilot, Bard, Luzia, and ChatGPT. Four clinical toxicologists were asked to answer the same questions. Twenty-four recruited experts in toxicology were sent a pair of answers (1 from an AI system and one from a toxicologist) for each of the 10 questions. For each answer, the experts had to identify the source, evaluate text quality, and assess level of knowledge reflected. Quantitative variables were described as mean (SD) and qualitative ones as absolute frequency and proportion. A value of P .05 was considered significant in all comparisons. RESULTS: Of the 240 evaluated AI answers, the expert evaluators thought that 21 (8.8%) and 38 (15.8%), respectively, were certainly or probably written by a toxicologist. The experts were unable to guess the source of 13 (5.4%) AI answers. Luzia and ChatGPT were better able to mislead the experts than Bard (P = .036 and P = .041, respectively). Text quality was judged excellent in 38.8% of the AI answers. ChatGPT text quality was rated highest (61.3% excellent) vs Bard (34.4%), Luzia (31.7%), and Copilot (26.3%) (P .001, all comparisons). The average score for the level of knowledge perceived in the AI answers was 7.23 (1.57) out of 10. The highest average score was achieved by ChatGPT at 8.03 (1.26) vs Luzia (7.02 [1,63]), Bard (6.91 [1.64]), and Copilot (6.91 [1.46]) (P .001, all comparisons). CONCLUSIONS: Luzia and ChatGPT answers to the toxicology questions were often thought to resemble those of clinical toxicologists. ChatGPT answers were judged to be very well-written and reflect a very high level of knowledge.

OBJETIVO: Formular preguntas sobre intoxicaciones a cuatro sistemas de inteligencia artificial (IA) y a cuatro toxicólogos clínicos (TC) y constatar si un grupo de observadores es capaz de identificar el origen de las respuestas. Valorar la calidad del texto y el nivel de conocimientos ofrecidos por estas IA y compararlos con el de los TC. METODO: Se prepararon 10 preguntas de toxicología y se introdujeron en cuatro sistemas de IA (Copilot, Bard, LuzIA y ChatGPT). Se solicitó a cuatro TC que respondiesen a las mismas preguntas. Se consiguieron 24 observadores expertos en toxicología y se les remitió un cuestionario con 10 preguntas y cada una de ellas con una respuesta procedente de una IA y otra de un TC. Cada observador tenía que decidir la procedencia de las respuestas, valorar la calidad del texto y cuantificar el nivel de conocimientos sobre el tema. RESULTADOS: De las 240 respuestas que analizaron los observadores y que procedían de alguna IA, en 21 ocasiones (8,8%) opinaron que con certeza provenían de un TC, en 38 (15,8%) que procedían probablemente de un TC y en 13 (5,4%) reconocían que no podían establecer el origen de la respuesta. LuzIA y ChatGPT mostraron una mayor capacidad de engaño a los observadores, con diferencias significativas respecto a Bard (p = 0,036 y p = 0,041, respectivamente). Con relación a la calidad de los textos de las respuestas ofrecidas por las IA, la valoración de los observadores fue de excelente en el 38,8% de las ocasiones, con una diferencia significativa en favor de ChatGPT (61,3% de respuestas excelentes) respecto a Bard (34,4%, p 0,001), LuzIA (31,7%, p 0,001) y Copilot (26,3%, p 0,001). Respecto a la percepción de conocimientos sobre el tema por parte de las IA, la puntuación media de fue de 7,23 (DE 1,57) sobre 10, obteniendo ChatGPT una puntuación de 8,03 (DE 1,26) que fue mayor a la obtenida por Luzia [7,02 (DE 1,63), p 0,001], Bard [6,91 (1,64), p 0,001] y Copilot [6,91 (1,46), p 0,001]. CONCLUSIONES: LuzIA y ChatGPT son sistemas de IA capaces de generar respuestas a preguntas de toxicología que, con frecuencia, parecen haber sido respondidas por un TC. La calidad de los textos generados y la percepción de conocimientos que ofrece ChatGPT es muy elevada.

New ultrasound techniques. Present and future.

The present study highlights the advances in ultrasound, especially regarding its clinical applications to critically ill patients. Artificial intelligence (AI) is crucial in automating image interpretation, improving accuracy and efficiency. Software has been developed to make it easier to perform accurate bedside ultrasound examinations, even by professionals lacking prior experience, with automatic image optimization. In addition, some applications identify cardiac structures, perform planimetry of the Doppler wave, and measure the size of vessels, which is especially useful in hemodynamic monitoring and continuous recording. The "strain" and "strain rate" parameters evaluate ventricular function, while "auto strain" automates its calculation from bedside images. These advances, and the automatic determination of ventricular volume, make ultrasound monitoring more precise and faster. The next step is continuous monitoring using gel devices attached to the skin.

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)

PROFUGO study protocol: Predictive model for the early diagnosis of anastomotic leak after esophagectomy and gastrectomy.

In esophagogastric surgery, the appearance of an anastomotic leak is the most feared complication. Early diagnosis is important for optimal management and successful resolution. For this reason, different studies have investigated the value of the use of markers to predict possible postoperative complications. Because of this, research and the creation of predictive models that identify patients at high risk of developing complications are mandatory in order to obtain an early diagnosis. The PROFUGO study (PRedictivO Model for Early Diagnosis of anastomotic LEAK after esophagectomy and gastrectomy) is proposed as a prospective and multicenter national study that aims to develop, with the help of artificial intelligence methods, a predictive model that allows for the identification of high-risk cases. of anastomotic leakage and/or major complications by analyzing different clinical and analytical variables collected during the postoperative period of patients undergoing esophagectomy or gastrectomy.

Evaluación de la fiabilidad y legibilidad de las respuestas de los chatbots como recurso de información al paciente para las exploraciones PET-TC más communes.

PURPOSE: This study aimed to evaluate the reliability and readability of responses generated by two popular AI-chatbots, 'ChatGPT-4.0' and 'Google Gemini', to potential patient questions about PET/CT scans. MATERIALS AND METHODS: Thirty potential questions for each of [18F]FDG and [68Ga]Ga-DOTA-SSTR PET/CT, and twenty-nine potential questions for [68Ga]Ga-PSMA PET/CT were asked separately to ChatGPT-4 and Gemini in May 2024. The responses were evaluated for reliability and readability using the modified DISCERN (mDISCERN) scale, Flesch Reading Ease (FRE), Gunning Fog Index (GFI), and Flesch-Kincaid Reading Grade Level (FKRGL). The inter-rater reliability of mDISCERN scores provided by three raters (ChatGPT-4, Gemini, and a nuclear medicine physician) for the responses was assessed. RESULTS: The median [min-max] mDISCERN scores reviewed by the physician for responses about FDG, PSMA and DOTA PET/CT scans were 3.5 [2-4], 3 [3-4], 3 [3-4] for ChatPT-4 and 4 [2-5], 4 [2-5], 3.5 [3-5] for Gemini, respectively. The mDISCERN scores assessed using ChatGPT-4 for answers about FDG, PSMA, and DOTA-SSTR PET/CT scans were 3.5 [3-5], 3 [3-4], 3 [2-3] for ChatGPT-4, and 4 [3-5], 4 [3-5], 4 [3-5] for Gemini, respectively. The mDISCERN scores evaluated using Gemini for responses FDG, PSMA, and DOTA-SSTR PET/CTs were 3 [2-4], 2 [2-4], 3 [2-4] for ChatGPT-4, and 3 [2-5], 3 [1-5], 3 [2-5] for Gemini, respectively. The inter-rater reliability correlation coefficient of mDISCERN scores for ChatGPT-4 responses about FDG, PSMA, and DOTA-SSTR PET/CT scans were 0.629 (95% CI = 0,32-0,812), 0.707 (95% CI = 0.458-0.853) and 0.738 (95% CI = 0.519-0.866), respectively (p < 0.001). The correlation coefficient of mDISCERN scores for Gemini responses about FDG, PSMA, and DOTA-SSTR PET/CT scans were 0.824 (95% CI = 0.677-0.910), 0.881 (95% CI = 0.78-0.94) and 0.847 (95% CI = 0.719-0.922), respectively (p < 0.001). The mDISCERN scores assessed by ChatGPT-4, Gemini, and the physician showed that the chatbots' responses about all PET/CT scans had moderate to good statistical agreement according to the inter-rater reliability correlation coefficient (p < 0,001). There was a statistically significant difference in all readability scores (FKRGL, GFI, and FRE) of ChatGPT-4 and Gemini responses about PET/CT scans (p < 0,001). Gemini responses were shorter and had better readability scores than ChatGPT-4 responses. CONCLUSION: There was an acceptable level of agreement between raters for the mDISCERN score, indicating agreement with the overall reliability of the responses. However, the information provided by AI-chatbots cannot be easily read by the public.

Efecto de la inteligencia emocional en la salud mental de los trabajadores del Poder Judicial en Iberoamérica / Effect of emotional intelligence on the mental health of workers in the Judiciary in Latin America / Efeito da inteligência emocional na saúde mental de trabalhadores do Judiciário na América Latin

El trabajador en las diferentes áreas del poder judicial contribuye a agilizar dicho servicio, mejorando la atención al público, agilizando procesos que redundan en beneficio de los justiciables y a la sociedad en general, en la realización de este trabajo están sometidos a condiciones de estrés, su salud metal depende en gran medida de su inteligencia emocional y de la forma que sea capaz de manejarla. Esta investigación, de enfoque cualitativo, tuvo como objetivo reflexionar sobre la información científica orientada a brindar una visión general de Inteligencia emocional a nivel de Iberoamérica y sus efectos en la salud mental. Para ello, se realizó una búsqueda sistemática en español e inglés con las palabras clave inteligencia emocional, poder judicial, salud metal; arrojando un total de 64900 artículos. Seguidamente, se realizó la depuración a través de la revisión de títulos y resúmenes, quedando 21 artículos. Finalmente, se llevó a cabo el examen del total de los documentos siendo 16 investigaciones las que contaban con todos los criterios de inclusión y calidad, pasando a formar parte del estudio. Las bases de datos empleadas para esta revisión fueron: Scopus, Science Direct, EBSCO, Proquest, Scielo y Redalyc. Los resultados de esta investigación determinaron que, con el beneficio de esta práctica, hubo de reducción de los niveles de agresión de tipo verbal, en comparación, a los efectos alcanzados en agresión física, hostilidad e ira, hacia los usuarios que cuando no maneja la inteligencia emociona

The worker in the different areas of the judiciary contributes to streamline said service, improving customer service, streamlining judicial processes that benefit the defendants and society in general. This research, with a qualitative approach, aimed to compare the different scientific articles aimed at providing an overview of Emotional Intelligence at the Ibero-American level. For this, a systematic search was carried out in Spanish and English with the keywords emotional intelligence, judicial power; yielding a total of 64900 articles. Subsequently, the debugging was carried out through the review of titles and abstracts, leaving 21 articles. Finally, a total examination of the documents was carried out, 16 investigations being those that had all the inclusion and quality criteria, becoming part of the study. The databases used for this review were: Scopus, Science Direct, EBSCO, Proquest, Scielo and Redalyc. The results of this research determined that, The results obtained showed a similarity in the research designs, which were carried out in their entirety with the non-experimental design. There was a general reduction in the levels of verbal aggression, in comparison, to the effects achieved in physical aggression, hostility and anger, when emotional intelligence is handled

O trabalhador nas diferentes áreas do poder judiciário contribui para agilizar o referido serviço, melhorando o atendimento ao público, agilizando processos judiciais que beneficiam os réus e a sociedade em geral. Esta investigação, de abordagem qualitativa, teve como objetivo comparar os diferentes artigos científicos que visam fornecer uma visão geral da inteligência emocional a nível da Ibero-América. Para tanto, foi realizada uma busca sistemática em espanhol e inglês com as palavras-chave inteligência emocional, poder judicial; rendendo um total de 64.900 artigos. Em seguida, foi realizada a purificação por meio da revisão de títulos e resumos, restando 21 artigos. Por fim, foi realizado o exame total dos documentos, sendo 16 investigações que atenderam a todos os critérios de inclusão e qualidade, passando a fazer parte do estudo. As bases de dados utilizadas para esta revisão foram: Scopus, Science Direct, EBSCO, Proquest, Scielo e Redalyc. Os resultados desta pesquisa determinaram que, Os resultados obtidos mostraram semelhança nos desenhos de pesquisa, que foram realizados integralmente com o desenho não experimental. Houve uma redução geral nos níveis de agressão verbal, em comparação, aos efeitos alcançados na agressão física, hostilidade e raiva, quando a inteligência emocional é gerenciada

Methodological evaluation of systematic reviews based on the use of artificial intelligence systems in chest radiography.

INTRODUCTION: In recent years, systems that use artificial intelligence (AI) in medical imaging have been developed, such as the interpretation of chest X-ray to rule out pathology. This has produced an increase in systematic reviews (SR) published on this topic. This article aims to evaluate the methodological quality of SRs that use AI for the diagnosis of thoracic pathology by simple chest X-ray. MATERIAL AND METHODS: SRs evaluating the use of AI systems for the automatic reading of chest X-ray were selected. Searches were conducted (from inception to May 2022): PubMed, EMBASE, and the Cochrane Database of Systematic Reviews. Two investigators selected the reviews. From each SR, general, methodological and transparency characteristics were extracted. The PRISMA statement for diagnostic tests (PRISMA-DTA) and AMSTAR-2 were used. A narrative synthesis of the evidence was performed. Protocol registry: Open Science Framework: https://osf.io/4b6u2/. RESULTS: After applying the inclusion and exclusion criteria, 7 SRs were selected (mean of 36 included studies per review). All the included SRs evaluated "deep learning" systems in which chest X-ray was used for the diagnosis of infectious diseases. Only 2 (29%) SRs indicated the existence of a review protocol. None of the SRs specified the design of the included studies or provided a list of excluded studies with their justification. Six (86%) SRs mentioned the use of PRISMA or one of its extensions. The risk of bias assessment was performed in 4 (57%) SRs. One (14%) SR included studies with some validation of AI techniques. Five (71%) SRs presented results in favour of the diagnostic capacity of the intervention. All SRs were rated "critically low" following AMSTAR-2 criteria. CONCLUSIONS: The methodological quality of SRs that use AI systems in chest radiography can be improved. The lack of compliance in some items of the tools used means that the SRs published in this field must be interpreted with caution.

Approaching artificial intelligence to Hospital Pharmacy. / Acercando la inteligencia artificial a los servicios de farmacia hospitalaria.

Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.

Tackling the future in hospital pharmacy: Training as a pillar of success. / Conquistando el futuro en farmacia hospitalaria: la formación como pilar del éxito.

The training of hospital pharmacists in the coming years must adapt and respond to constant current and future social and technological challenges, without neglecting the basic areas of the profession. It is necessary to acquire knowledge in what is known as digital comprehensive health: Artificial intelligence, technology and automation, digital skills, and new forms of communication with patients, such as telemedicine and telepharmacy that are already a reality in many hospitals. We must provide knowledge in automated systems for the distribution and dispensing of medicines, robots for preparing sterile preparations, traceability systems, the use of drones in clinical care, etc., as well as including training in the application of technology in pharmaceutical care, through devices and applications that help identify patients who require specific care early and effectively. In this digital scenario, new risks and challenges must be faced, such as cybersecurity and cyber-resilience, which makes the training and education of healthcare professionals in general, and hospital pharmacists in particular, essential. On the other hand, the appearance of increasingly complex and innovative therapies has a great impact not only on health population but also on economic and environmental issues, which makes new competencies and skills essential to develop and implement disruptive and competent financing, equity, and sustainability strategies. In this demanding and hyper-connected environment, it is understandable that the well-known "burned out worker syndrome" appears, which prevents the correct personal and professional development of the team and highlights the importance of quality training for its prevention and management. In short, in the next decade, the training of hospital pharmacists must be aimed at providing knowledge in innovation and in basic skills needed to adapt and succeed to current demands and changes.

[Translated article] Introducing artificial intelligence to hospital pharmacy departments.

Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.

[Translated article] Conquering the future in hospital pharmacy: Training as a pillar of success.

The training of hospital pharmacists in the coming years must adapt and respond to constant current and future social and technological challenges, without neglecting the basic areas of the profession. It is necessary to acquire knowledge in what is known as digital comprehensive health: artificial intelligence, technology and automation, digital skills, and new forms of communication with patients, such as telemedicine and telepharmacy that are already a reality in many hospitals. We must provide knowledge in automated systems for the distribution and dispensing of medicines, robots for preparing sterile preparations, traceability systems, the use of drones in clinical care, etc. as well as training in the application of technology in pharmaceutical care, through devices and applications that help identify patients who require specific care early and effectively. In this digital scenario, new risks and challenges must be faced, such as cybersecurity and cyber resilience, which makes the training and education of healthcare professionals in general, and hospital pharmacists in particular, inexcusable. On the other hand, the appearance of increasingly complex and innovative therapies has a great impact not only on health population but also on economic and environmental issues, which makes new competencies and skills essential to develop and implement disruptive and competent financing, equity, and sustainability strategies. In this demanding and hyper-connected environment, it is understandable that the well-known "burned out worker syndrome" appears, which prevents the correct personal and professional development of the team and highlights the importance of quality training for its prevention and management. In short, in the next decade, the training of hospital pharmacists must be aimed at providing knowledge in innovation and in basic skills needed to adapt and succeed to current demands and changes.

[Translated article] The role of artificial intelligence in scientific publishing: perspectives from hospital pharmacy.

The article examines the impact of artificial intelligence on scientific writing, with a particular focus on its application in hospital pharmacy. It analyses 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 emphasised. 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, optimise 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.

[Translated article] Bioethical Conflicts in Current Dermatology: A Narrative Review.

Both the functions and equipment of dermatologists have increased over the past few years, some examples being cosmetic dermatology, artificial intelligence, tele-dermatology, and social media, which added to the pharmaceutical industry and cosmetic selling has become a source of bioethical conflicts. The objective of this narrative review is to identify the bioethical conflicts of everyday dermatology practice and highlight the proposed solutions. Therefore, we conducted searches across PubMed, Web of Science and Scopus databases. Also, the main Spanish and American deontological codes of physicians and dermatologists have been revised. The authors recommend declaring all conflicts of interest while respecting the patients' autonomy, confidentiality, and privacy. Cosmetic dermatology, cosmetic selling, artificial intelligence, tele-dermatology, and social media are feasible as long as the same standards of conventional dermatology are applied. Nonetheless, the deontological codes associated with these innovations need to be refurbished.

Efecto de la inteligencia emocional en la salud mental de los trabajadores del Poder Judicial en Iberoamérica

El trabajador en las diferentes áreas del poder judicial contribuye a agilizar dicho servicio, mejorando la atención al público, agilizando procesos que redundan en beneficio de los justiciables y a la sociedad en general, en la realización de este trabajo están sometidos a condiciones de estrés, su salud metal depende en gran medida de su inteligencia emocional y de la forma que sea capaz de manejarla. Esta investigación, de enfoque cualitativo, tuvo como objetivo reflexionar sobre la información científica orientada a brindar una visión general de Inteligencia emocional a nivel de Iberoamérica y sus efectos en la salud mental. Para ello, se realizó una búsqueda sistemática en español e inglés con las palabras clave inteligencia emocional, poder judicial, salud metal; arrojando un total de 64900 artículos. Seguidamente, se realizó la depuración a través de la revisión de títulos y resúmenes, quedando 21 artículos. Finalmente, se llevó a cabo el examen del total de los documentos siendo 16 investigaciones las que contaban con todos los criterios de inclusión y calidad, pasando a formar parte del estudio. Las bases de datos empleadas para esta revisión fueron: Scopus, Science Direct, EBSCO, Proquest, Scielo y Redalyc. Los resultados de esta investigación determinaron que, con el beneficio de esta práctica, hubo de reducción de los niveles de agresión de tipo verbal, en comparación, a los efectos alcanzados en agresión física, hostilidad e ira, hacia los usuarios que cuando no maneja la inteligencia emocional.

The worker in the different areas of the judiciary contributes to streamline said service, improving customer service, streamlining judicial processes that benefit the defendants and society in general. This research, with a qualitative approach, aimed to compare the different scientific articles aimed at providing an overview of Emotional Intelligence at the Ibero-American level. For this, a systematic search was carried out in Spanish and English with the keywords emotional intelligence, judicial power; yielding a total of 64900 articles. Subsequently, the debugging was carried out through the review of titles and abstracts, leaving 21 articles. Finally, a total examination of the documents was carried out, 16 investigations being those that had all the inclusion and quality criteria, becoming part of the study. The databases used for this review were: Scopus, Science Direct, EBSCO, Proquest, Scielo and Redalyc. The results of this research determined that, The results obtained showed a similarity in the research designs, which were carried out in their entirety with the non-experimental design. There was a general reduction in the levels of verbal aggression, in comparison, to the effects achieved in physical aggression, hostility and anger, when emotional intelligence is handled.

O trabalhador nas diferentes áreas do poder judiciário contribui para agilizar o referido serviço, melhorando o atendimento ao público, agilizando processos judiciais que beneficiam os réus e a sociedade em geral. Esta investigação, de abordagem qualitativa, teve como objetivo comparar os diferentes artigos científicos que visam fornecer uma visão geral da inteligência emocional a nível da Ibero-América. Para tanto, foi realizada uma busca sistemática em espanhol e inglês com as palavras-chave inteligência emocional, poder judicial; rendendo um total de 64.900 artigos. Em seguida, foi realizada a purificação por meio da revisão de títulos e resumos, restando 21 artigos. Por fim, foi realizado o exame total dos documentos, sendo 16 investigações que atenderam a todos os critérios de inclusão e qualidade, passando a fazer parte do estudo. As bases de dados utilizadas para esta revisão foram: Scopus, Science Direct, EBSCO, Proquest, Scielo e Redalyc. Os resultados desta pesquisa determinaram que, Os resultados obtidos mostraram semelhança nos desenhos de pesquisa, que foram realizados integralmente com o desenho não experimental. Houve uma redução geral nos níveis de agressão verbal, em comparação, aos efeitos alcançados na agressão física, hostilidade e raiva, quando a inteligência emocional é gerenciada.

Artificial Intelligence-Based Diagnosis of Obstructive Sleep Apnea Syndrome: A Scoping Review / Diagnóstico Basado en Inteligencia Artificial para el Síndrome Obstructivo de Apnea del Sueño: Una Revisión de Alcance

SUMMARY: To diagnose obstructive sleep apnea syndrome (OSAS), polysomnography is used, an expensive and extensive study requiring the patient to sleep in a laboratory. OSAS has been associated with features of facial morphology, and a preliminary diagnosis could be made using an artificial intelligence (AI) predictive model. This study aimed to analyze, using a scoping review, the AI-based technological options applied to diagnosing OSAS and the parameters evaluated in such analyses on craniofacial structures. A systematic search of the literature was carried out up to February 2024, and, using inclusion and exclusion criteria, the studies to be analyzed were determined. Titles and abstracts were independently selected by two researchers. Fourteen studies were selected, including a total of 13,293 subjects analyzed. The age of the sample ranged from 18 to 90 years. 9,912 (74.56 %) subjects were male, and 3,381 (25.43 %) were female. The included studies presented a diagnosis of OSAS by polysomnography; seven presented a control group of subjects without OSAS and another group with OSAS. The remaining studies presented OSAS groups in relation to their severity. All studies had a mean accuracy of 80 % in predicting OSAS using variables such as age, gender, measurements, and/or imaging measurements. There are no tests before diagnosis by polysomnography to guide the user in the likely presence of OSAS. In this sense, there are risk factors for developing OSA linked to facial shape, obesity, age, and other conditions, which, together with the advances in AI for diagnosis and guidance in OSAS, could be used for early detection.

Para diagnosticar el Síndrome Apnea Obstructiva del Sueño (SAOS) se utiliza la polisomnografía, el cual es un costoso y extenso estudio que exige que el paciente duerma en un laboratorio. El SAOS ha sido asociado con características de la morfología facial y mediante un modelo predictivo de la Inteligencia Artificial (IA), se podría realizar un diagnóstico preliminar. El objetivo de este estudio fue analizar por medio de una revisión de alcance, las opciones tecnológicas basadas en IA aplicadas al diagnóstico del SAOS, y los parámetros evaluados en dichos análisis en las estructuras craneofaciales. Se realizó una búsqueda sistemática de la literatura hasta febrero del 2024 y mediante criterios de inclusión y exclusión se determino los estudios a analizar. Los títulos y resúmenes fueron seleccionados de forma independiente por dos investigadores. Se seleccionaron 14 estudios, incluyeron un total de 13.293 sujetos analizados. El rango edad de la muestra oscilo entre 18 y 90 años. 9.912 (74.56 %) sujetos eran de sexo masculino y 3.381 (25,43 %) eran de sexo femenino. Los estudios incluidos presentaron diagnóstico de SAOS mediante polisomnografía, siete estudios presentaron un grupo control de sujetos con ausencia de SAOS y otro grupo con presencia de SAOS. Mientras que los demás estudios, presentaron grupos de SAOS en relación con su severidad. Todos los estudios tuvieron una precisión media del 80 % en la predicción de SAOS utilizando variables como la edad, el género, mediciones y/o mediciones imagenológicas. no existen exámenes previos al diagnóstico por polisomnografía que permitan orientar al usuario en la probable presencia de SAOS. En este sentido, existen factores de riesgo para desarrollar SAOS vinculados a la forma facial, la obesidad, la edad y otras condiciones, que sumados a los avances con IA para diagnóstico y orientación en SAOS podrían ser utilizados para la detección precoz del mismo.

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)

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.

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