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)

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)

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.

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 DiscoveryTM 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 DiscoveryTM 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 DiscoveryTM system shows similar diagnostic performance to VCE with iSCAN in detecting colonic dysplasia in ulcerative colitis patients at risk for colorectal cancer.

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)

[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.

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.

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.

Bioética y democracia en la sociedad digital / Bioètica i democràcia a la societat digital / Bioethics and democracy in digital society

La sociedad contemporánea vive la revolución digital y la necesidad de reflexionar sobre la interacción entre los seres humanos y las tecnologías digitales. El auge de las tecnologías de inteligencia artificial y la algoritmización social ha planteado interrogantes sobre la indispensabilidad de la supervisión y el análisis ético de la información y los datos en Internet. Así como la necesidad de verificar la influencia de las plataformas digitales en el ejercicio de la ciudadanía. La bioética posibilita la investigación sobre los principios que se deben respetar en una sociedad democrática y digital. Resaltamos los principios de responsabilidad social y no discriminación con la intención de que los beneficios del uso tecnológico promuevan el bienestar y la calidad de vida de los menos favorecidos. Su objetivo es garantizar la supervivencia de la especie humana y la mejora de la protección de la vida de todos los seres vivos, animales y plantas. La reflexión bioética sobre el uso de la inteligencia artificial podría establecer la brújula moral que oriente el análisis de los conflictos éticos y la defensa de que a todos los seres humanos se les debe garantizar la igualdad de oportunidades y las condiciones para realizar plenamente su proyecto de vida.(AU)

La societat contemporània viu la revolució digital i la necessitat de reflexionar sobre la interacció entre els éssers humans i les tecnologies digitals. L'auge de les tecnologies d'intel·ligència artificial i la algoritmització social ha plantejat interrogants sobre la indispensabilitat de la supervisió i l'anàlisi ètic de la informació i les dades a Internet. Així com la necessitat de verificar la influència de les plataformes digitals en l'exercici de la ciutadania. La bioètica possibilita la recerca sobre els principis que es deuen respectar en una societat democràtica i digital. Destaquem els principis de responsabilitat social i no discriminació amb la intenció que els beneficis de l'ús tecnològic promoguin el benestar i la qualitat de vida dels menys afavorits. El seu objectiu és garantir la supervivència de l'espècie humana i la millora de la protecció de la vida de tots els éssers vius, animals i plantes. La reflexió bioètica sobre l'ús de la intel·ligència artificial podria establir la brúixola moral que orienti l'anàlisi dels conflictes ètics i la defensa que a tots els éssers humans se'ls ha de garantir la igualtat d'oportunitats i les condicionsper realitzar plenament el seu projecte de vida.(AU)

Contemporary society is going through the digital revolution and the need to reflect on the interaction between human beings and digital technologies. The rise of artificial intelligence technologies and social algorithmization has raised questions about the need for ethical monitoring and analysis of information and data on the Internet. As well as the need to verify the influence of digital platforms in the exercise of citizenship. Bioethics enables research on the principles that must be respected in a democratic and digital society. We highlight the principles of social responsibility and non-discrimination with the intention that the benefits of technological use promote the well-being and quality of life of the less favored. Its objective is to guarantee the survival of the human species and the improvement of the protection of the life of all living beings, animals, and plants. Bioethical reflection on the use of artificial intelligence could establish the moral compass that guides the analysis of ethical conflicts and the defense that all human beings must be guaranteed equal opportunities and the conditions to fully carry out their project of life.(AU)

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.

Tratamiento de la informacion de violencia de género. Con aportaciones de la inteligencia artificial / Treatment of the information on gender-based violence. With contributions from artificial intelligence

Introducción/objetivos la violencia contra la mujer sigue siendo un grave problema social y de salud a pesar de las medidas puestas en marcha en los últimos años. La exploración de las víctimas por el médico forense en los juzgados es de gran interés puesto que recibe información relacionada no solo con la agresión, sino también de su entorno social, familiar y económico. El objetivo es utilizar dicha información para identificar grupos de riesgo y mejorar/obtener las medidas necesarias. Material y métodos en este trabajo, el forense ha recogido, durante 8 años, una toma abundante de datos sobre las víctimas exploradas en L’Hospitalet de Llobregat. La muestra incluye 1.622 casos de mujeres víctimas de violencia de género. Se realiza un estudio descriptivo poblacional y de las lesiones. Resultados se exponen las principales variables estudiadas tanto socioeconómicas como referentes a la agresión en sí. Se trabaja también con base en la reentrada de las víctimas o repetición de las agresiones (revictimización), que son el 10,9% de la muestra. Finalmente, se presentan los resultados obtenidos tras aplicar técnicas de inteligencia artificial, en este caso, árboles de clasificación CaRT. Conclusiones con los resultados obtenidos concluimos que el tratamiento de la información recogida y sistematizada de la intervención médico-forense permite una mejor comprensión de la violencia sobre la mujer, de la que podemos extraer sugerencias sobre la adopción de medidas de atención y soporte a las víctimas y a los colectivos más vulnerables, así como sobre los recursos administrativos y la optimización de programas de prevención. (AU)

Introduction/objectives Violence against women is still a serious social and health problem, despite the measures implemented in recent years. The examination of the victims by the forensic doctor in the courts is of great interest since it provides information related not only to the aggression, but also to their social, family and economic environment. The objective is to use this information to identify groups at risk and improve/implement the necessary measures. Material and methods In this work, the forensic has collected, for eight years, abundant data on the victims examined in L'Hospitalet de Llobregat. The sample includes 1,622 cases of women who have been victims of gender violence. A descriptive study of the population and of the lesions has been carried out. Results The paper presents the main variables studied, both socioeconomic and referring to the aggression itself. This study also analyzes the reentry of the victims, the repetition of aggressions (revictimization), which are 10.9% of the sample. Finally, the results obtained after applying artificial intelligence techniques -in this case, CaRT classification trees- are presented. Conclusions With the results obtained, we conclude that the treatment of the information collected and systematized from the medical-forensic intervention allows a better understanding of Violence Against Women, from which we can extract suggestions on the adoption of care and support measures for the victims and the most vulnerable groups, as well as administrative resources and the optimization of prevention programs. (AU)

Ética e inteligencia artificial / Ethics and artificial intelligence

La relación entre ética e inteligencia artificial en medicina es un tema crucial y complejo y se encuadra en su contexto más amplio. Así, la ética en inteligencia artificial médica implica asegurar que las tecnologías sean seguras, justas y respeten la privacidad de los pacientes. Esto incluye preocuparse de la precisión de los diagnósticos proporcionados por la inteligencia artificial, la equidad en el tratamiento de pacientes y la protección de los datos personales de salud. Los avances en inteligencia artificial pueden mejorar significativamente la atención médica, desde diagnósticos más precisos hasta tratamientos personalizados. Sin embargo, es esencial que los desarrollos en inteligencia artificial médica se realicen con una consideración ética fuerte, involucrando a los pacientes, profesionales de la salud e inteligencia artificial y especialistas en ética para guiar y supervisar su implementación. Por último, es fundamental la transparencia en los algoritmos de inteligencia artificial y la formación continua para los profesionales médicos. (AU)

The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics in medical artificial intelligence involves ensuring that technologies are safe, fair, and respect patient privacy. This includes concerns about the accuracy of diagnoses provided by artificial intelligence, fairness in patient treatment, and protection of personal health data. Advances in artificial intelligence can significantly improve healthcare, from more accurate diagnoses to personalized treatments. However, it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration, involving healthcare professionals, artificial intelligence experts, patients, and ethics specialists to guide and oversee their implementation. Finally, transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental. (AU)

Inteligencia artificial en pediatría: actualidad y retos / Artificial intelligence in paediatrics: Current events and challenges

Se examina el uso de la inteligencia artificial (IA) en el campo de la atención a la salud pediátrica dentro del marco de la «Medicina de las 7P» (Predictiva, Preventiva, Personalizada, Precisa, Participativa, Periférica y Poliprofesional). Se destacan diversas aplicaciones de la IA en el diagnóstico, el tratamiento y el control de enfermedades pediátricas, así como su papel en la prevención y en la gestión eficiente de los recursos médicos con su repercusión en la sostenibilidad de los sistemas públicos de salud. Se presentan casos de éxito de la aplicación de la IA en el ámbito pediátrico y se hace un gran énfasis en la necesidad de caminar hacia la Medicina de las 7P. La IA está revolucionando la sociedad en general ofreciendo un gran potencial para mejorar significativamente el cuidado de la salud en pediatría.(AU)

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.(AU)

[Analysis of the challenges and dilemmas that bioethics of the 21st century will face in the digital health era]. / Análisis de retos y dilemas que deberá afrontar la bioética del siglo xxi, en la era de la salud digital.

The medical history underscores the significance of ethics in each advancement, with bioethics playing a pivotal role in addressing emerging ethical challenges in digital health (DH). This article examines the ethical dilemmas of innovations in DH, focusing on the healthcare system, professionals, and patients. Artificial Intelligence (AI) raises concerns such as confidentiality and algorithmic biases. Mobile applications (Apps) empower but pose challenges of access and digital literacy. Telemedicine (TM) democratizes and reduces healthcare costs but requires addressing the digital divide and interconsultation dilemmas; it necessitates high-quality standards with patient information protection and attention to equity in access. Wearables and the Internet of Things (IoT) transform healthcare but face ethical challenges like privacy and equity. 21st-century bioethics must be adaptable as DH tools demand constant review and consensus, necessitating health science faculties' preparedness for the forthcoming changes.

Ideas to use CHATGPT in the Prevention of Occupational Risks / Ideas para usar CHATGPT en Prevención de Riesgos Laborales

In this work, some ideas are presented to use ChatGPT in PRL such as: analyze the history of accidents and risks, supervise real -time job security, provide detailed information on the use of hazardous equipment, make risk simulations, among others . The article highlights the benefits of ChatGPT, such as its accessibility, consistency, efficiency, flexibility, customization and ability to identify patterns and trends. However, some limitations and challenges are also mentioned, such as the need for human supervision, precise data dependence and ethical and privacy concerns. The article concludes that ChatGPT can be a valuable tool for the prevention of occupational hazards, but must be used in combination with other techniques and tools for effective risk prevention. In addition, the importance of considering the social and ethical impacts of technology and of promoting diversity and competition in the artificial intelligence market stands out.

En este trabajo, se presentan ideas para utilizar CHATGPT en PRL como pueden ser: analizar el historial de accidentes y riesgos, supervisar la seguridad laboral en tiempo real, proporcionar información detallada sobre el uso de equipos peligrosos, realizar simulaciones de riesgos, entre otras. El artículo destaca los beneficios de CHATGPT, como su accesibilidad, consistencia, eficiencia, flexibilidad, personalización y capacidad para identificar patrones y tendencias. Sin embargo, también se mencionan algunas limitaciones y desafíos, como la necesidad de supervisión humana, la dependencia de datos precisos y las preocupaciones éticas y de privacidad. El artículo concluye que CHATGPT puede ser una herramienta valiosa para la prevención de riesgos laborales, pero debe ser utilizada en combinación con otras técnicas y herramientas para una prevención efectiva de riesgos. Además, se destaca la importancia de considerar los impactos sociales y éticos de la tecnología y de fomentar la diversidad y la competencia en el mercado de la inteligencia artificial.

Bioethical conflicts in current dermatology: a narrative review. / Conflictos bioéticos en la dermatología actual: una revisión narrativa.

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.

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.

Evaluation of vital signs through a mobile application in patients with heart failure: a opportunity for remote titration?

BACKGROUND: Virtual consultations have increased exponentially, but a limitation is the inability to assess vital signs (VS). This is particularly useful in patients with heart failure (HF) for titrating prognosis-modifying medication. This issue could potentially be addressed by a tool capable of measuring blood pressure (BP) and heart rate (HR) accurately, remotely, and conveniently. Mobile phones equipped with transdermal optical imaging technology could meet these requirements. OBJECTIVE: To evaluate the accuracy of a transdermal optical imaging-based app for estimating VS compared to clinical assessment in patients with HF. METHODS: A prospective cohort study included patients evaluated in an HF outpatient unit between February and April 2022. BP and HR were simultaneously assessed using the app and clinical examination (BP with an automated sphygmomanometer and HR by brachial palpation). Three measurements were taken by both the app and clinic for each patient, by two independent blinded physicians. RESULTS: Thirty patients were included, with 540 measurements of BP and HR. The mean age was 66 (± 13) years, 53.3% were male. The mean left ventricular ejection fraction was 37 ± 15, with 63.3% having previous hospitalizations for HF, and 63.4% in NYHA class II-III. The mean difference between the app measurement and its clinical reference measurement was 3.6 ± 0.5 mmHg for systolic BP (SBP), 0.9 ± -0.2 mmHg for diastolic BP (DBP), and 0.2 ± 0.4 bpm for HR. When averaging the paired mean differences for each patient, the mean across the 30 patients was 2 ± 6 mmHg for SBP, -0.14 ± 4.6 mmHg for DBP, and 0.23 ± 4 bpm for HR. CONCLUSION: The estimation of BP and HR by an app with transdermal optical imaging technology was comparable to non-invasive measurement in patients with HF and met the precision criteria for BP measurement in this preliminary study. The use of this new transdermal optical imaging technology provides promising data, which should be corroborated in larger cohorts.

ANTECEDENTES: Las consultas virtuales aumentaron exponencialmente, pero presentan como limitación la imposibilidad de valorar los signos vitales (SV), siendo especialmente útiles en los pacientes con insuficiencia cardiaca (IC) para titular medicación que modifica pronóstico. Este problema podría potencialmente solucionarse mediante una herramienta que pueda medir la presión arterial (PA) y frecuencia cardiaca (FC) de manera precisa, accesible y remota. Los teléfonos móviles equipados con tecnología de imágenes ópticas transdérmicas podrían cumplir con estos requisitos. OBJETIVO: Evaluar la precisión de una app basada en imagen óptica transdérmica para estimar SV en relación con la valoración clínica en pacientes con IC. MÉTODOS: Estudio de cohorte prospectivo, se incluyeron pacientes evaluados en una unidad ambulatoria de IC de febrero a abril del 2022. Se valoró simultáneamente la PA y FC mediante la app y el examen clínico (PA con un esfigmomanómetro automatizado y FC por palpación braquial). Se realizaron tres mediciones por app y clínica en cada paciente, por dos médicos independientes, encontrándose ciegos a los resultados. RESULTADOS: Se incluyeron 30 pacientes, con 540 mediciones de TA y de FC. Edad media de 66 (± 13) años, el 53.3% de sexo masculino. La fracción de eyección del ventrículo izquierdo media fue de 37 ± 15, con hospitalizaciones previas por IC el 63.3%, en CF II-III el 63.4%. La diferencia media entre la medición de la app y su medición de referencia clínica fue de 3.6 ± 0.5 mmHg para PA sistólica (PAS), 0.9 ± ­0.2 mmHg para PA diastólica (PAD) y 0.2 ± 0.4 lpm para FC. Cuando se promedian las diferencias medias emparejadas para cada paciente, la media entre los 30 pacientes es de 2 ± 6 mmHg para PAS, ­0.14 ± 4.6 mmHg para PAD y 0.23 ± 4 lpm para FC. CONCLUSIÓN: La estimación de PA y FC por una app con tecnología de imagen óptica transdérmica fue comparable a la medición no invasiva en pacientes con IC, y cumple los criterios de precisión de la medición de PA en este estudio preliminar. La utilización de esta nueva tecnología de imagen óptica transdérmica brinda datos prometedores, que deberán ser corroborados en cohortes de mayor tamaño.