Comparison of the refractive prediction errors of artificial intelligence formula with 3 conventional formulas and 11 combination methods in cataract surgery on eyes with short axial length

ABSTRACT Purpose: To compare the refractive prediction error of Hill-radial basis function 3.0 with those of 3 conventional formulas and 11 combination methods in eyes with short axial lengths. Methods: The refractive prediction error was calculated using 4 formulas (Hoffer Q, SRK-T, Haigis, and Hill-RBF) and 11 combination methods (average of two or more methods). The absolute error was determined, and the proportion of eyes within 0.25-diopter (D) increments of absolute error was analyzed. Furthermore, the intraclass correlation coefficients of each method were computed to evaluate the agreement between target refractive error and postoperative spherical equivalent. Results: This study included 87 eyes. Based on the refractive prediction error findings, Hoffer Q formula exhibited the highest myopic errors, followed by SRK-T, Hill-RBF, and Haigis. Among all the methods, the Haigis and Hill-RBF combination yielded a mean refractive prediction error closest to zero. The SRK-T and Hill-RBF combination showed the lowest mean absolute error, whereas the Hoffer Q, SRK-T, and Haigis combination had the lowest median absolute error. Hill-radial basis function exhibited the highest intraclass correlation coefficient, whereas SRK-T showed the lowest. Haigis and Hill-RBF, as well as the combination of both, demonstrated the lowest proportion of refractive surprises (absolute error >1.00 D). Among the individual formulas, Hill-RBF had the highest success rate (absolute error ≤0.50 D). Moreover, among all the methods, the SRK-T and Hill-RBF combination exhibited the highest success rate. Conclusions: Hill-radial basis function showed accuracy comparable to or surpassing that of conventional formulas in eyes with short axial lengths. The use and integration of various formulas in cataract surgery for eyes with short axial lengths may help reduce the incidence of refractive surprises.

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.

Conectando el cuerpo, transhumanismo y agroecología. Mapeo científico 2000-2024 y análisis crítico / Connecting the body, transhumanism and agroecology. Scientific mapping 2000-2024 and critical analysis / Conectando o corpo, o transhumanismo e a agroecologia. Mapeamento científico 2000-2024 e análise crítica

RESUMEN El objetivo esta investigación es realizar un mapeo científico basado en la teoría de grafos y efectuar un análisis crítico de la producción científica relacionada con la temática de transhumanismo y agroecología. Metodológicamente se hizo una búsqueda del tema en la plataforma Scopus, las referencias extraídas se procesaron mediante la herramienta Bibliometrix. Posteriormente, se hizo un análisis crítico que contrastara la perspectiva del cuerpo desde el transhumanismo y desde la agroecología. Los resultados muestran que los países con mayor producción científica en el área son China, Estados Unidos y Francia. También señalan que hay un interés creciente de la comunidad científica en el tema de transhumanismo y agroecología, que se evidencia en una marcada producción académica en los últimos cinco años. El artículo contrasta dos visiones del cuerpo: una visión reduccionista e instrumental impuesta por el Occidente hegemónico y una perspectiva agroecológica, que considera el cuerpo como un territorio profundo e inconmensurable, conectado con la tierra, el agua, el aire, el fuego, las plantas, los minerales, los animales, y el cosmos.

ABSTRACT This research aims to carry out a scientific mapping based on graph theory and conduct a critical analysis of the scientific production related to transhumanism and agroecology Methodologically, a search for the topic was carried out on the Scopus platform, the extracted references were processed using the Bibliometrix tool. Subsequently, a critical analysis was made that contrasted the perspective of the body from transhumanism and agroecology The results show that the countries with the greatest scientific production in the area are China, the United States, and France. They also point out a growing interest from the scientific community in the topic of transhumanism and agroecology which is evident in a marked academic production in the last five years. The article contrasts two visions of the body: a reductionist and instrumental vision imposed by the hegemonic West and an agroecological perspective, which considers the body as a deep and immeasurable territory connected with the earth, water air, fire, and plants. , minerals, animals, and the cosmos.

RESUMO O objetivo desta pesquisa é realizar um mapeamento científico com base na teoria dos grafos e fazer uma análise crítica da produção científica relacionada aos temas do transhumanismo e da agroecologia. Metodologicamente, foi realizada uma busca sobre o tema na plataforma Scopus, e as referências extraídas foram processadas por meio da ferramenta Bibliometrix. Posteriormente, foi feita uma análise crítica para contrastar a perspectiva do corpo a partir do transhumanismo e da agroecologia. Os resultados mostram que os países com maior produção científica na área são China, Estados Unidos e França. Apontam também que há um crescente interesse da comunidade científica pelo tema do transhumanismo e da agroecologia, o que é evidenciado por uma acentuada produção acadêmica nos últimos cinco anos. O artigo contrapõe duas visões do corpo: uma visão reducionista e instrumental imposta pelo Ocidente hegemônico e uma perspectiva agroecológica, que considera o corpo como um território profundo e imensurável, conectado à terra, à água, ao ar, ao fogo, às plantas, aos minerais, aos animais e ao cosmos.

Retos y perspectivas en la investigación biomédica con datos en la Unión Europea en la Era Digital / Challenges and perspectives in biomedical research with data in the European Union in the Digital Era / Desafios e perspectivas na pesquisa biomédica com dados na União Europeia na Era Digital

La Era Digital en la que vivimos exige el replanteamiento de los paradigmas jurídicos actuales, tanto desde el punto de vista de la regulación como de la gobernanza, y esto afecta de forma particular a los datos, y a la investigación científica con datos. Este trabajo trata de realizar una panorámica a los desafíos y oportunidades que se plantean en este ámbito, buscando una metodología de investigación jurídica multidisciplinar que se adecúe al emergente Derecho (europeo) de la ciencia y la tecnología. Concluye la necesidad de trabajar para que en el desarrollo de este nuevo Derecho, tanto la regulación como la gobernanza siga una serie de principios éticos y jurídicos que permitan la garantía de los derechos y libertades de las personas.

The Digital Age in which we live requires the rethinking of current legal paradigms, both from the point of view of regulation and governance, and this particularly affects data, and scientific research with data. This work attempts to provide an overview of the challenges and opportunities that arise in this area, seeking a multidisciplinary legal research methodology adapted to the emerging (European) law of science and technology. It concludes the need to work so that in its development, both regulation and governance follow a series of ethical and legal principles that allow the guarantee of the rights and freedoms of people.

A Era Digital em que vivemos exige repensar os paradigmas jurídicos atuais, tanto do ponto de vista da regulação como da governação, e isso afeta particularmente os dados, e a investigação científica com dados. Este trabalho tenta fornecer uma visão geral dos desafios e oportunidades que surgem nesta área, procurando uma metodologia de investigação jurídica multidisciplinar que se adapte ao emergente Direito (europeu) da ciência e da tecnologia. Conclui a necessidade de trabalhar para que no seu desenvolvimento, tanto a regulação como a governança sigam uma série de princípios éticos e legais que permitam a garantia dos direitos e liberdades das pessoas.

Los Neurodatos y su protección frente a la Inteligencia Artificial y las Neurotecnologías / Os Neurodados e sua proteção frente à Inteligência Artificial e às Neurotecnologias / Neurodata and its protection from Artificial Intelligence and Neurotechnologies

Los neurodatos, es decir los datos provenientes del examen de la actividad cerebral humana y del sistema nervioso, pueden ser recolectados por distintas neurotecnologías con uso de inteligencia Artificial tanto en el ámbito médico, desde el punto de vista diagnostico especialmente mediante electroencefalografía, interfaz cerebro-computador, resonancia nuclear magnética funcional etc., pero también en la terapias sanitarias y en la actividad de rehabilitación; en el marketing y en la prestación de servicios a los consumidores (por ejemplo los videojuegos y otras aplicaciones lúdicas), en aplicaciones con fines de seguridad, hasta su uso en el proceso penal o con fines militares. Esta investigación intenta dilucidar desde el punto de vista jurídico la naturaleza y alcance de los neurodatos con especial énfasis en la pregunta si pueden considerarse como datos personales o si es necesaria una regulación específica como la chilena.

Os neurodados, ou seja, os dados provenientes do exame da atividade cerebral humana e do sistema nervoso, podem ser coletados por distintas neurotecnologias com o uso de inteligência artificial, tanto no âmbito médico, sob o ponto de vista diagnóstico, especialmente por meio de eletroencefalografia, interface cérebro-computador, ressonância magnética funcional, etc., como também em terapias de saúde e na atividade de reabilitação; no marketing e na prestação de serviços aos consumidores (por exemplo, em videogames e outras aplicações lúdicas), em aplicações com fins de segurança, até seu uso em processos penais ou para fins militares. Esta pesquisa busca esclarecer, do ponto de vista jurídico, a natureza e o alcance dos neurodados, com especial ênfase na questão de saber se podem ser considerados como dados pessoais ou se é necessária uma regulamentação específica, como a chilena.

Neurodata, i.e. data from the examination of human brain activity and the nervous system, can be collected by different neurotechnologies with the use of Artificial Intelligence both in the medical field, from the diagnostic point of view especially through electroencephalography, brain-computer interface, functional magnetic resonance imaging etc., but also in health therapies and rehabilitation activity; in marketing and consumer services (e.g. video games and other entertainment applications), in applications for security purposes, to their use in criminal prosecution or for military purposes. This research attempts to elucidate from a legal point of view the nature and scope of neurodata with special emphasis on the question whether they can be considered as personal data or whether a specific regulation such as the Chilean one is necessary.

The European Union legal framework for using artificial intelligence and imaging databases and imaging biobanks for research purposes: applying the notion of fairness / O quadro jurídico da União Europeia para o uso de inteligência artificial e bases de dados de imagens e biobancos de imagens para fins de pesquisa: aplicando a noção de justiça / El marco legal de la Unión Europea para el uso de inteligencia artificial y bases de datos de imágenes y biobancos de imágenes con fines de investigación: aplicando la noción de equidad

Objective: To analyze the issue of justice and discrimination in artificial intelligence systems based on medical image databases. Methodology: Analysis of documents that constitute the regulatory framework of the European Union for the use of artificial intelligence, compared with the report FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. Results: The study indicates that artificial intelligence trained with unbalanced data tends to generate biased predictions, which can exacerbate health inequalities and affect justice. Discrimination in artificial intelligence systems appears abstract, subtle, and difficult to detect compared to traditional forms of discrimination. Final Considerations: Robust regulation is necessary to ensure justice in artificial intelligence systems, considering the need for interdisciplinary collaboration to prepare this new generation of legal professionals with an enhanced perspective on the topic and its various dimensions.

Objetivo: analisar a questão da justiça e da discriminação em sistemas de inteligência artificial, com base em bancos de imagens médicas. Metodologia: análise de documentos que compõem o marco normativo da União Europeia para o uso da inteligência artificial cotejados com o relatório FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. Resultados: o estudo indica que a inteligência artificial treinada com dados desbalanceados tende a gerar previsões enviesadas, o que pode exacerbar desigualdades de saúde e afetar a justiça. A discriminação em sistemas de inteligências artificias se mostra abstrata, sutil e de difícil detecção quando comparadas com as formas tradicionais de discriminação. Considerações finais: Impõe-se uma regulamentação robusta para garantir justiça nos sistemas de inteligência artificial considerando a necessidade de colaboração interdisciplinar para preparar essa nova geração de juristas com um olhar aprimorado sobre o tema e suas variadas dimensões.

Objetivo: Analizar la cuestión de la justicia y la discriminación en sistemas de inteligencia artificial, basándose en bancos de imágenes médicas. Metodología: Análisis de documentos que constituyen el marco normativo de la Unión Europea para el uso de la inteligencia artificial, cotejados con el informe FUTURE-AI: Principios Rectores y Recomendaciones de Consenso para una Inteligencia Artificial Confiable en Imágenes Médicas. Resultados: El estudio indica que la inteligencia artificial entrenada con datos desbalanceados tiende a generar predicciones sesgadas, lo que puede exacerbar las desigualdades en salud y afectar la justicia. La discriminación en los sistemas de inteligencia artificial se muestra abstracta, sutil y de difícil detección en comparación con las formas tradicionales de discriminación. Consideraciones finales: Es necesaria una regulación robusta para garantizar la justicia en los sistemas de inteligencia artificial, considerando la necesidad de colaboración interdisciplinaria para preparar a esta nueva generación de juristas con una perspectiva mejorada sobre el tema y sus diversas dimensiones.

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.

From Anatomy to Algorithm: scope of AI-Assisted Diagnostic Competencies in Health Sciences Education / De la Anatomía al Algoritmo: alcance de las Competencias Diagnósticas Asistidas por Inteligencia Artificial en la Educación en Ciencias de la Salud

The article explores the evolution of medical knowledge from its anatomical and functional foundations to the integration of advanced technological tools, focusing on the impact of artificial intelligence (AI) on the development of diagnostic competencies. Initially, medical training relied on direct observation and clinical judgment based on anatomical and surgical knowledge. Subsequently, the inclusion of physiology and pathology enabled a functional understanding of the human body, transforming diagnosis into a systematic skill supported by objective data such as laboratory tests and medical imaging. The integration of AI in recent decades has revolutionized this process, offering unprecedented capabilities to analyze complex clinical data. Tools such as machine learning algorithms and predictive systems have enhanced diagnostic precision, allowing for the identification of previously unnoticed patterns. This data-driven approach strengthens physicians' ability to correlate clinical symptoms and signs with specific pathological entities. However, the incorporation of AI presents challenges in medical education. Future physicians must combine learning traditional clinical foundations with mastering advanced technologies, all while maintaining an ethical and patient-centered approach. Furthermore, excessive reliance on technology and biases inherent in algorithms underscore the need to balance technological innovation with human clinical judgment. The article highlights that medical education must adapt to include critical competencies such as digital literacy, ethical reasoning, and critical thinking. AI-based simulators and educational platforms are playing a key role in preparing physicians for a more digitized clinical environment, while research remains essential to ensure transparency and fairness in these technologies.

El artículo explora la evolución del conocimiento médico desde sus bases anatómicas y funcionales hasta la integración de herramientas tecnológicas avanzadas, con un enfoque en el impacto de la inteligencia artificial (IA) en el desarrollo de competencias diagnósticas. En sus inicios, la formación médica dependía de la observación directa y el juicio clínico basado en el conocimiento anatómico y quirúrgico. Posteriormente, la inclusión de fisiología y patologías permitió una comprensión funcional del cuerpo humano, transformando el diagnóstico en una habilidad sistemática apoyada por datos objetivos como pruebas de laboratorio e imágenes médicas. La incorporación de la IA en las últimas décadas ha revolucionado este proceso, proporcionando capacidades sin precedentes para analizar datos clínicos complejos. Herramientas como algoritmos de aprendizaje automático y sistemas predictivos han elevado la precisión del diagnóstico, permitiendo identificar patrones que antes pasaban desapercibidos. Este enfoque basado en datos refuerza la capacidad del médico para correlacionar síntomas y signos clínicos con entidades patológicas específicas. Sin embargo, la integración de la IA plantea desafíos en la educación médica. Los futuros médicos deben combinar el aprendizaje de fundamentos clínicos tradicionales con el dominio de tecnologías avanzadas, todo ello mientras mantienen un enfoque ético y centrado en el paciente. Además, la dependencia excesiva en la tecnología y los sesgos inherentes a los algoritmos subrayan la necesidad de un equilibrio entre innovación tecnológica y juicio clínico humano. El artículo destaca que la formación médica debe adaptarse para incluir competencias críticas como alfabetización digital, razonamiento ético y pensamiento crítico. Los simuladores y plataformas educativas basadas en IA están desempeñando un papel clave en la preparación de los médicos para un entorno clínico más digitalizado, mientras que la investigación sigue siendo esencial para garantizar la transparencia y equidad de estas tecnologías.

Artificial intelligence-driven patient monitoring for adverse event detection in clinical trials

Artificial intelligence (AI) keeps an eye on people in clinical studies to find out when bad things happen. This is a big way that AI is changing healthcare. It goes into a lot of detail about how AI has changed this field and stresses how important it is to use complicated formulas, always keep an eye on things, and follow the rules. These days, we have tools like deep learning frameworks, controlled and unsupervised learning models, and others that help us find bad things faster and more accurately. Tracking in real time is possible with early warning systems and constant data analysis. It helps make sure the experiment is done right and puts the safety of the people being tested first. AI-driven tracking systems can only work in an honest and reliable way if they follow the rules set by regulatory bodies such as the FDA and the EMA. The fact that AI has the ability to change the way medical research is done today, with benefits like making it faster and more accurate, makes its problems even more important. The report comes to the conclusion that more research, better teamwork, and a wider use of AI technologies are needed to make it more reliable to find bad events in clinical studies over time.

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.

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.

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.

Investigación global sobre uso de inteligencia artificial en imagenología para la detección de cáncer de mama: Análisis bibliométrico / Global research on use of artificial intelligence in imaging for breast cancer detection: bibliometric analysis

RESUMEN Introducción: El cáncer de mama sigue siendo uno de los cánceres más frecuentes a nivel global, específicamente, el más frecuente en el sexo femenino. El uso de inteligencia artificial promete contribuir al diagnóstico precoz, a través de la imagenología. Previamente, no se ha descrito el panorama y avance de esta producción científica. Métodos: Estudio bibliométrico de corte transversal, que usó Scopus como fuente de datos. Se utilizó el paquete bibliometrix de R para el cálculo de indicadores bibliométricos y visualización de los resultados. Resultados: Se seleccionaron 1292 documentos, publicados entre 1989 y 2024. El 75,3% (n=973) fueron artículos con datos primarios, seguido de un 16,2% (n=209) correspondiente a revisiones. Se identificó una colaboración internacional del 26,5%, y un crecimiento anual de la producción del 10,78%. Se observó que, la clasificación de riesgo por screening, tomosíntesis digital de la mama, aprendizaje por transferencia, segmentación y selección por características, son las palabras clave más comúnmente usadas. En los últimos cinco años, el aprendizaje profundo y la mamografía, han sido los temas con mayor popularidad. La colaboración internacional, ha sido liderada por Estados Unidos, China y Reino Unido. Conclusiones: Se identificó un crecimiento notable en la investigación global sobre el uso de inteligencia artificial en imagenología para la detección de cáncer de mama, marcado a partir de la década del 2010, esencialmente por medio de publicación de artículos con datos primarios. La relación entre inteligencia artificial e imagenología para diagnóstico de cáncer de mama, se ha centrado en riesgo y predicción.

ABSTRACT Introduction: Breast cancer remains one of the most prevalent cancers globally, specifically the most common in females. The use of artificial intelligence promises to contribute to early diagnosis through imaging. Previously, the landscape and evolution of this scientific production have not been described. Methods: Cross-sectional bibliometric study using Scopus as the data source. The bibliometrix package in R was employed for calculating bibliometric indicators and visualizing the results. Results: 1292 documents published between 1989 and 2024 were selected. 75.3% (n=973) were articles with primary data, followed by 16.2% (n=209) corresponding to reviews. An international collaboration rate of 26.5% was identified, with an annual production growth of 10.78%. It was observed that risk classification through screening, digital breast tomosynthesis, transfer learning, segmentation, and feature selection were the most commonly used keywords. In the last five years, deep learning and mammography have been the most popular topics. International collaboration has been led by the United States, China, and the United Kingdom. Conclusion: A notable growth in global research on the use of artificial intelligence in breast cancer imaging for detection was identified, particularly since the 2010s, primarily through the publication of articles with primary data. The relationship between artificial intelligence and imaging for breast cancer diagnosis has focused on risk and prediction.

Identification of Spices and their Adulterants by Integrating Machine Learning and Analytical Techniques: A Representative Study

This multidisciplinary research presents a comprehensive method to tackle the widespread problem of spice adulteration, which represents substantial risks to both public health and spices authenticity. A comprehensive approach is developed to authenticate spices with high accuracy and efficiency by combining old methods with contemporary approaches such as machine learning and artificial intelligence. This paper presents a specific case study where machine learning models, specifically using transfer learning with proven frameworks like MobileNetV2, were effectively employed. The models achieved an impressive accuracy of 98.67% in identifying Capsicum annum, a spice that is usually adulterated in the market. In addition, a wide range of traditional and advanced techniques, including qualitative testing, microscopy, colorimetry, density measurement, and spectroscopy, are reviewed closely. In addition, this article provides a detailed explanation of high-performance liquid chromatography based quantitation of capsaicin, which is the main active constituent for ascertaining the quality of C. annum. The present work defines a new interdisciplinary approach and also provides valuable information on evaluating the quality of spices and identifying adulterants using artificial intelligence. The outcomes presented here have the potential to completely transform the methods used to verify the authenticity of spices and herbal drugs, therefore ensuring the safety and health of consumers by confirming the quality.

Trends and patterns of orthognathic surgery in Saudi Arabia: a visualized bibliometric analysis of research output

This study conducted a bibliometric analysis of orthognathic surgery research from Saudi Arabia between 1994 and 2024 in the Web of Science database. The goal was to evaluate Saudi Arabia's influence in the field and implications worldwide. Relevant keywords were used without year restrictions to search for articles. Biblioshiny and VOS viewer were used to analyse and visualize the bibliometric data, including total citations, h-index, and number of papers. The results show that Saudi Arabia has significantly increased its orthognathic surgery research production over time, especially in 2021 and 2023. King Saud University and King Abdulaziz University emerged as the leading institutions in number of papers. The most cited work was an expert review on using artificial intelligence for orthodontic diagnosis and planning orthognathic surgeries. This represents meaningful progress in combining technology with orthognathic surgery. Alhammad, Alnofaie, and Al-Sebaei were identified as the most productive individual authors, each authoring around three papers. The bibliographic analysis highlights the need for increased cooperation between Saudi institutions to boost research outputs and advance the application of new technologies in orthognathic surgery. The study serves as a foundation for further developing orthognathic surgery research in Saudi Arabia, which remains one of the few developing nations showing promising potential for growth in this area.

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.

Automatic Auditing System for Endoscopic Exploration of the Stomach with Artificial Intelligence-Gastro UNAL: Gastroendoscopy UNit for Automatic Labeling / Sistema de auditoría automática para la exploración endoscópica del estómago con Inteligencia Artificial - Gastro UNAL: Gastroendoscopy UNit for Automatic Labeling

Abstract Introduction: Upper endoscopy is the standard method for diagnosing early-stage gastric cancer. However, according to estimates, up to 20% of tumors are not detected, and their accuracy may be affected by the variability in their performance. In Colombia, most diagnoses take place in advanced stages, which aggravates the problem. Protocols have been proposed to ensure the complete observation of areas prone to premalignant lesions to address variability. Objective: To build and validate an automatic audit system for endoscopies using artificial intelligence techniques. Methodology: In this study, 96 patients from a teaching hospital underwent video-documented endoscopies, spanning 22 stations rearranged to minimize overlaps and improve the identification of 13 key gastric regions. An advanced convolutional network was used to process the images, extracting visual characteristics, which facilitated the training of artificial intelligence in the classification of these areas. Results: the model, called Gastro UNAL, was trained and validated with images of 67 patients (70% of cases) and tested with 29 different patients (30% of cases), which reached an average sensitivity of 85,5% and a specificity of 98,8% in detecting the 13 gastric regions. Conclusions: The effectiveness of the model suggests its potential to ensure the quality and accuracy of endoscopies. This approach could confirm the regions evaluated, alerting less experienced or trained endoscopists about blind spots in the examinations, thus, increasing the quality of these procedures.

Resumen Introducción: La endoscopia digestiva alta es el método estándar para diagnosticar el cáncer gástrico en etapas tempranas. Sin embargo, su precisión puede verse afectada por la variabilidad en su realización, y se estiman hasta 20% de tumores no detectados. En Colombia, la mayoría de los diagnósticos se realizan en etapas avanzadas, lo que agrava el problema. Para abordar la variabilidad, se han propuesto protocolos con el fin de asegurar la observación completa de áreas propensas a lesiones premalignas. Objetivo: Construir y validar un sistema de auditoría automática para endoscopias usando técnicas de inteligencia artificial. Metodología: En este estudio, 96 pacientes de un hospital universitario se sometieron a endoscopias documentadas en video, abarcando 22 estaciones reorganizadas para minimizar solapamientos y mejorar la identificación de 13 regiones gástricas clave. Se utilizó una red convolucional avanzada para procesar las imágenes, extrayendo características visuales, lo que facilitó el entrenamiento de la inteligencia artificial en la clasificación de estas áreas. Resultados: El modelo, llamado Gastro UNAL, fue entrenado y validado con imágenes de 67 pacientes (70% de los casos) y probado con 29 pacientes distintos (30% de los casos), con lo que alcanzó una sensibilidad promedio del 85,5% y una especificidad del 98,8% en la detección de las 13 regiones gástricas. Conclusiones: La eficacia del modelo sugiere su potencial para asegurar la calidad y precisión de las endoscopias. Este enfoque podría confirmar las regiones evaluadas, alertando puntos ciegos en la exploración a los endoscopistas con menos experiencia o en entrenamiento, de tal forma que se aumente la calidad de estos procedimientos.

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

La era de la inteligencia artificial y su impacto en el aprendizaje de habilidades quirúrgicas de grado y posgrado: ¿El futuro de la cirugía? / The age of artificial intelligence and its impact on learning surgical skills: is it the future of surgery?

RESUMEN Los cambios en la educación desafían a los profesores sobre cómo enseñar de la mejor manera y mejorar el desempeño de sus estudiantes. En el caso de la cirugía es necesario adquirir habilidades manuales que reflejen el pensamiento crítico y la capacidad de tomar decisiones en situaciones complejas, de manera rápida y eficaz. Así, la inteligencia artificial (IA) es una nueva herramienta que puede mejorar el desempeño de los estudiantes de grado y posgrado, así como repercutir en mejores desenlaces clínicos. El papel que debe desempeñar la enseñanza tradicional y el futuro de la enseñanza quirúrgica son cuestiones para resolver.

ABSTRACT Educational changes present a challenge for teachers in terms of how to effectively teach and enhance student performance. Surgery demands manual dexterity that reflects critical thinking and the ability to make efficient decisions quickly in complex situations. Artificial Intelligence (AI) is a tool that can enhance the performance of both undergraduate and graduate students and improve clinical outcomes. The role of traditional teaching and the future of surgical education need to be addressed.