La inteligencia artificial en los procesos editoriales y la evaluación por pares / Artificial Intelligence in the Editorial Process and Peer Review

Resumen La narrativa mitológica de Epimeteo y Prometeo, retratada por Platón, sirve de introducción a la importancia de la inteligencia artificial (IA). El hombre se caracteriza en este mito, frente al resto de criaturas, por tener un don divino: la capacidad de crear herramientas. La IA representa un avance revolucionario al sustituir la labor intelectual humana, destacando su capacidad para generar nuevo conocimiento de forma autónoma. En el ámbito científico, la IA agiliza la revisión por pares y mejora la eficiencia en la evaluación de manuscritos, además de aportar elementos creativos, como la reescritura, traducción o creación de ilustraciones. Sin embargo, su implementación debe ser ética, limitada a un asistente y bajo la supervisión experta para evitar errores y abusos. La IA, una herramienta divina en evolución, requiere que cada uno de sus avances se estudie y aplique críticamente.

Abstract The mythological story of Epimetheus and Prometheus, as told by Plato, serves as an introduction to the meaning of artificial intelligence (AI). In this myth, man, unlike other creatures, is endowed with a divine gift: the ability to create tools. AI represents a revolutionary advance, replacing human intellectual labour and emphasising its ability to autonomously generate new knowledge. In the scientific field, AI is speeding up peer review processes and increasing the efficiency of manuscript evaluation, while also contributing creative elements such as rewriting, translating or creating illustrations. However, its use must be ethical, limited to an assisting role, and subject to expert oversight to prevent errors and misuse. AI, an evolving divine tool, requires critical study and application of each of its advances.

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.

The legal regulation of artificial intelligence in the European Union: A practical guide for radiologists.

The European Union is taking the lead globally on the regulation of Artificial Intelligence (AI) and developing important legislation, namely the AI Act. The purpose of this article is to describe this regulation and examine three implications that will affect radiologists. In relation to the' risk approach', AI applications in radiology will be classified as high risk, thus necessitating compliance with a series of requirements and obligations. Secondly,' effective radiologist supervision' involves establishing supervision-automation levels, defining an appropriate degree of authority, and determining how AI recommendations will be documented in the radiological report. Finally, this article examines the different forms of' legal liability' that radiologists may incur in the event of a diagnostic error made by combined radiologist-artificial intelligence.

Digital health in cardiovascular medicine: An overview of key applications and clinical impact by the Portuguese Society of Cardiology Study Group on Digital Health.

Digital health interventions including telehealth, mobile health, artificial intelligence, big data, robotics, extended reality, computational and high-fidelity bench simulations are an integral part of the path toward precision medicine. Current applications encompass risk factor modification, chronic disease management, clinical decision support, diagnostics interpretation, preprocedural planning, evidence generation, education, and training. Despite the acknowledged potential, their development and implementation have faced several challenges and constraints, meaning few digital health tools have reached daily clinical practice. As a result, the Portuguese Society of Cardiology Study Group on Digital Health set out to outline the main digital health applications, address some of the roadblocks hampering large-scale deployment, and discuss future directions in support of cardiovascular health at large.

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.

Assistentes Virtuais Inteligentes e saúde mental: debates regulatórios no Brasil / Intelligent Virtual Assistants and mental health: regulatory debates in Brazil / Asistentes Virtuales Inteligentes y salud mental: debates regulatorios en Brasil

A inserção dos Assistentes Virtuais Inteligentes na vida cotidiana representa um marco na história da comunicação entre humanos e máquinas. Devido às suas características interativas, estes estão sendo cada vez mais apropriados e desenvolvidos para fins de cuidado, especialmente no âmbito da saúde mental. Este artigo visa compreender se e como o debate regulatório brasileiro oferece instrumentos para lidar com os desafios e as preocupações desses sistemas de Inteligência Artificial em relação à saúde mental. A partir de uma análise documental, mapeamos exemplos de aplicação dos Assistentes Virtuais Inteligentes em saúde mental, a fim de identificar riscos a direitos dos usuários e avaliar, na legislação brasileira vigente e em discussão, se há proteção suficiente para lidar com eles. Por meio de uma abordagem crítica, salientamos a insuficiência da legislação brasileira atual e a necessidade de ampliação do debate sobre como equilibrar possíveis riscos e benefícios dessas tecnologias.

The integration of Intelligent Virtual Assistants into everyday life marks a milestone in the history of human-machine communication. Due to their interactive characteristics, they are increasingly being appropriated and developed for caregiving purposes, especially in the field of mental health. This article aims to understand whether and how the Brazilian regulatory debate provides tools to address the challenges and concerns of these Artificial Intelligence systems concerning mental health. Through a document analysis, we map examples of Intelligent Virtual Assistants's applications to mental health to identify risks to users' rights and evaluate whether the current and the proposed Brazilian legislation offer sufficient protection to address these risks. Through a critical approach, we highlight the inadequacy of current Brazilian legislation and the need to expand the debate on how to balance the potential risks and benefits of these technologies.

La inserción de los Asistentes Virtuales Inteligentes en la vida cotidiana representa un hito en la historia de la comunicación entre humanos y máquinas. Debido a sus características interactivas, cada vez son más apropiados y desarrollados para fines de cuidado. Este artículo tiene como objetivo comprender si y cómo el debate regulatorio brasileño ofrece instrumentos para abordar los desafíos y preocupaciones de estos sistemas de Inteligencia Artificial en relación con la salud mental. A partir de un análisis documental, mapeamos ejemplos de la aplicación de los Asistentes Virtuales Inteligentes a la salud mental, con el fin de identificar riesgos para los derechos de los usuarios y evaluar, en la legislación brasileña vigente y en discusión, si hay protección suficiente para abordarlos. Destacamos la insuficiencia de la legislación brasileña actual y la necesidad de ampliar el debate sobre cómo equilibrar los posibles riesgos y beneficios de estas tecnologías.

[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.

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.

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.

Potential of artificial intelligence to generate health research reports of decayed, missed and restored teeth / Potencial de la inteligencia artificial para generar informes de investigación sanitaria sobre dientes cariados, perdidos y restaurados

Abstract This study aims to indicate the potential of artificial intelligence (AI) in epidemiological reports of decayed, missed and restored teeth. As a proof of concept our study model used panoramic x-ray images and an AI algorithm for tooth numbering, detection of the caries and restorations with accuracy over 80% for such diagnostic tasks. The output came as the number of decayed, missed and restored teeth according to patient's age and the DMFT index (number of decayed, missing, and filled teeth) which varied from 3.6 (up to 20 years old) to 20.4 (+60 years old). Thus, it is suggested that AI is a promising method to automate health data collection through the analysis of x-rays.

Resumen Este estudio tiene como objetivo indicar el potencial de la inteligencia artificial (IA) en los informes epidemiológicos de dientes cariados, perdidos y restaurados. Como prueba de concepto, nuestro modelo de estudio utilizó imágenes panorámicas de rayos X y un algoritmo de inteligencia artificial para la numeración de dientes, la detección de caries y las restauraciones con una precisión superior al 80 % para dichas tareas de diagnóstico. El resultado fue el número de dientes cariados, perdidos y restaurados según la edad del paciente y el índice CPOD (número de dientes cariados, perdidos y obturados) que varió de 3,6 (hasta 20 años) a 20,4 (+60 años). Por tanto, se sugiere que la IA es un método prometedor para automatizar la recopilación de datos de salud mediante el análisis de rayos X.

Aplicaciones, oportunidades y desafíos de implementar la inteligencia artificial en medicina: una revisión narrativa de la literatura / Applications, opportunities and challenges of implementing artificial intelligence in medicine: a narrative literature review

La inteligencia artificial se está usando ampliamente en diversos campos de la medicina. El objetivo de esta revisión es describir las principales aplicaciones, oportunidades y desafíos de la inteligencia artificial en medicina brindando una perspectiva del contexto actual. Se realizó una revisión narrativa de la literatura, identificando la información más actualizada y relevante sobre el tema. Se consultaron las bases de datos electrónicas PubMed, Scopus y SciELO, desde enero de 2019 a marzo de 2024, tanto en inglés como en español. Se incluyeron revisiones sistemáticas y no sistemáticas de la literatura, scoping reviews, artículos originales y capítulos de libros. Se excluyeron artículos duplicados, trabajos científicos poco claros, aquellos de bajo rigor científico y literatura gris. La implementación de la inteligencia artificial en medicina ha traído consigo notables beneficios, que van desde el registro de información médica hasta el descubrimiento de nuevos fármacos. Ha generado una revolución en la forma tradicional de hacer medicina. Por otro lado, ha traído consigo desafíos en materia de precisión, confiabilidad, ética, privacidad, entre otros. Es crucial mantener un enfoque centrado en el paciente y garantizar que estas tecnologías se utilicen para mejorar los resultados en salud y promover la equidad en el acceso a la atención médica. La colaboración entre profesionales de la salud, investigadores, entidades reguladoras y desarrolladores de tecnología será fundamental para enfrentar estos desafíos y aprovechar todo el potencial de la inteligencia artificial.

Artificial intelligence is being widely used in various fields of medicine. The aim of this review is to describe the main applications, opportunities and challenges of AI in medicine by providing an overview of the current context. An overview of the literature was conducted, identifying the most up-to-date and relevant information on the topic. The electronic databases PubMed, Scopus and SciELO were consulted, from January 2019 to March 2024, in both English and Spanish. Systematic and non-systematic literature reviews, scoping reviews, original articles and book chapters were included. Duplicate articles, unclear scientific papers, those of low scientific rigour and grey literature were excluded. The implementation of artificial intelligence in medicine has brought remarkable benefits, ranging from the recording of medical information to the discovery of new drugs. It has generated a revolution in the traditional way of doing medicine. On the other hand, it has brought with it challenges in terms of accuracy, reliability, ethics, privacy, among others. It is crucial to maintain a patient-centred approach and ensure that these technologies are used to improve health outcomes and promote equity in access to care. Collaboration between healthcare professionals, researchers, regulators and technology developers will be critical to address these challenges and realise the full potential of artificial intelligence.

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.

O uso de sistemas de inteligência artificial para a personalização da experiência do paciente: a percepção de gestores de tecnologia e inovação de hospitais associados à ANAHP / The use of artificial intelligence systems for patient experience personalization: the perception of technology and innovation managers at hospitals associated with ANAHP

Objetivo: O presente trabalho explora a percepção de gestores das áreas de Tecnologia e Inovação de hospitais privados brasileiros acerca do uso da inteligência artificial (IA) na saúde, com foco específico na personalização da experiência do paciente nesses hospitais. Métodos: Este trabalho se caracteriza como uma pesquisa descritiva transversal quantitativa. Foi desenvolvido um questionário com 14 questões que foi distribuído a uma amostra de gestores de tecnologia e inovação em hospitais, com o apoio da Associação Nacional de Hospitais Privados (ANAHP). O questionário foi disponibilizado em versão online à base de 122 hospitais associados à ANAHP. Resultados: Foram obtidas 30 respostas completas (aproximadamente 25% da base total), conquistando percepções sobre as vantagens, desvantagens e desafios éticos e técnicos relacionados ao emprego da IA na área clínica, particularmente em ambientes hospitalares. As respostas coletadas ratificaram o otimismo e a reserva dos profissionais de tecnologia e inovação em hospitais privados quanto ao poder e aos impactos da IA na personalização da experiência do paciente, bem como indicaram a necessidade de treinamento adequado para os funcionários desses hospitais, a fim de maximizar os benefícios da IA como ferramenta de apoio à tomada de decisão. Conclusões: Este trabalho é uma fonte de consulta para instituições de saúde que considerem utilizar a IA na personalização da experiência do paciente e queiram estabelecer treinamentos de pessoal baseados nesses princípios. Desse modo, os resultados aqui obtidos oferecem orientações valiosas para a adoção plena de IA no setor de saúde.

Objective: This study explores the perception of managers in the Technology and Innovation areas of Brazilian private hospitals regarding the use of artificial intelligence (AI) in healthcare, specifically focusing on patient experience personalization in these hospitals. Methods: This study is characterized as a quantitative cross-sectional descriptive research. A questionnaire with 14 questions was developed and distributed to a sample of technology and innovation managers in hospitals, with the support of the National Association of Private Hospitals (NAPH). The questionnaire was made available online to a base of 122 hospitals associated with NAPH. Results: 30 complete responses were obtained (nearly 25% of the total base), capturing perceptions on the advantages, disadvantages, and ethical and technical challenges related to the use of AI in clinical settings, particularly in hospital environments. The collected responses affirmed the optimism and caution of technology and innovation professionals in private hospitals regarding the power and impacts of AI on patient experience personalization, and indicated the need for adequate training for employees in these hospitals to maximize the benefits of AI as a decision support tool. Conclusions: This study serves as a reference for healthcare institutions considering the use of AI in patient experience personalization and aiming to establish personnel training based on these principles. Thus, the results obtained here offer valuable guidance for the full adoption of AI in the healthcare sector.

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.