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.

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.

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.

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

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)

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.

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.

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.

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)

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.

[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.

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.

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)

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.

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.

[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.