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

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.

Consideraciones Éticas y Académicas sobre el Uso de Inteligencia Artificial en Cursos de Metodología de la Investigación en Rehabilitación / Ethical and Academic Reflections on the Integration of Artificial Intelligence in Research Methodology Courses in Rehabilitation Science

En el cruce entre la revolución tecnológica y la educación en ciencias de la rehabilitación y del movimiento humano, la inteligencia artificial (IA) emerge como herramienta transformadora en los cursos de metodología de investigación. Este artículo destaca su potencial para optimizar la experiencia de aprendizaje y personalizar la instrucción, pero enfatiza la necesidad crucial de abordar desafíos éticos y pedagógicos. Propone orientaciones para equilibrar la innovación educativa y la responsabilidad académica, resaltando la importancia de la implementación consciente y planificada de la IA en los equipos de investigación en ciencias de la rehabilitación y del movimiento humano, garantizando así la integridad científica y ética en este campo en constante evolución.

In the intersection between technological advancements and education in rehabilitation science, artificial intelligence (AI) emerges as a transformative tool in research methodology. This article navigates the ethical and academic considerations tied to the incorporation of AI in rehabilitation and movement science courses. While acknowledging its potential to enhance learning experiences, it critically addresses the imperative to tackle ethical and pedagogical challenges. The paper offers guidance to strike a balance between educational innovation and academic responsibility. It emphasizes the need for a conscientious and planned implementation of AI, ensuring both scientific integrity and ethical adherence in this dynamically evolving field.

Evaluación de signos vitales mediante una aplicación móvil en pacientes con insuficiencia cardiaca: ¿una oportunidad para la titulación remota? / Evaluation of vital signs through a mobile application in patients with heart failure: a opportunity for remote titration?

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

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

Optimización de la interpretación de ecocardiogramas con la utilización de Machine Learning en el estudio WASE / Optimizing Echocardiograms Interpretation with the Application of Machine Learning in the WASE Study

RESUMEN Introducción: El número creciente de estudios ecocardiográficos y la necesidad de cumplir rigurosamente con las recomendaciones de guías internacionales de cuantificación, ha llevado a que los cardiólogos deban realizar tareas sumamente extensas y repetitivas, como parte de la interpretación y análisis de cantidades de información cada vez más abrumadoras. Novedosas técnicas de machine learning (ML), diseñadas para reconocer imágenes y realizar mediciones en las vistas adecuadas, están siendo cada vez más utilizadas para responder a esta necesidad evidente de automatización de procesos. Objetivos: Nuestro objetivo fue evaluar un modelo alternativo de interpretación y análisis de estudios ecocardiográficos, basado fundamentalmente en la utilización de software de ML, capaz de identificar y clasificar vistas y realizar mediciones estandarizadas de forma automática. Material y métodos: Se utilizaron imágenes obtenidas en 2000 sujetos normales, libres de enfermedad, de los cuales 1800 fueron utilizados para desarrollar los algoritmos de ML y 200 para su validación posterior. Primero, una red neuronal convolucional fue desarrollada para reconocer 18 vistas ecocardiográficas estándar y clasificarlas de acuerdo con 8 grupos (stacks) temáticos. Los resultados de la identificación automática fueron comparados con la clasificación realizada por expertos. Luego, algoritmos de ML fueron desarrollados para medir automáticamente 16 parámetros de eco Doppler de evaluación clínica habitual, los cuales fueron comparados con las mediciones realizadas por un lector experto. Finalmente, comparamos el tiempo necesario para completar el análisis de un estudio ecocardiográfico con la utilización de métodos manuales convencionales, con el tiempo necesario con el empleo del modelo que incorpora ML en la clasificación de imágenes y mediciones ecocardiográficas iniciales. La variabilidad inter e intraobservador también fue analizada. Resultados: La clasificación automática de vistas fue posible en menos de 1 segundo por estudio, con una precisión de 90 % en imágenes 2D y de 94 % en imágenes Doppler. La agrupación de imágenes en stacks tuvo una precisión de 91 %, y fue posible completar dichos grupos con las imágenes necesarias en 99% de los casos. La concordancia con expertos fue excelente, con diferencias similares a las observadas entre dos lectores humanos. La incorporación de ML en la clasificación y medición de imágenes ecocardiográficas redujo un 41 % el tiempo de análisis y demostró menor variabilidad que la metodología de interpretación convencional. Conclusión: La incorporación de técnicas de ML puede mejorar significativamente la reproducibilidad y eficiencia de las interpretaciones y mediciones ecocardiográficas. La implementación de este tipo de tecnologías en la práctica clínica podría resultar en reducción de costos y aumento en la satisfacción del personal médico.

ABSTRACT Background: The growing number of echocardiographic tests and the need for strict adherence to international quantification guidelines have forced cardiologists to perform highly extended and repetitive tasks when interpreting and analyzing increasingly overwhelming amounts of data. Novel machine learning (ML) techniques, designed to identify images and perform measurements at relevant visits, are becoming more common to meet this obvious need for process automation. Objectives: Our objective was to evaluate an alternative model for the interpretation and analysis of echocardiographic tests mostly based on the use of ML software in order to identify and classify views and perform standardized measurements automatically. Methods: Images came from 2000 healthy subjects, 1800 of whom were used to develop ML algorithms and 200 for subsequent validation. First, a convolutional neural network was developed in order to identify 18 standard echocardiographic views and classify them based on 8 thematic groups (stacks). The results of automatic identification were compared to classification by experts. Later, ML algorithms were developed to automatically measure 16 Doppler scan parameters for regular clinical evaluation, which were compared to measurements by an expert reader. Finally, we compared the time required to complete the analysis of an echocardiographic test using conventional manual methods with the time needed when using the ML model to classify images and perform initial echocardiographic measurements. Inter- and intra-observer variability was also analyzed. Results: Automatic view classification was possible in less than 1 second per test, with a 90% accuracy for 2D images and a 94% accuracy for Doppler scan images. Stacking images had a 91% accuracy, and it was possible to complete the groups with any necessary images in 99% of cases. Expert agreement was outstanding, with discrepancies similar to those found between two human readers. Applying ML to echocardiographic imaging classification and measurement reduced time of analysis by 41% and showed lower variability than conventional reading methods. Conclusion: Application of ML techniques may significantly improve reproducibility and efficiency of echocardiographic interpretations and measurements. Using this type of technologies in clinical practice may lead to reduced costs and increased medical staff satisfaction.

Implicancias de la inteligencia artificial en los métodos de imagen endovascular / Implications of Artificial Intelligence in Intravascular Imaging Methods

RESUMEN La angioplastia transluminal coronaria (ATC) es una de las principales estrategias de revascularización en pacientes con enfermedad coronaria aterosclerótica (ECA). Numerosos estudios respaldan la optimización de la ATC mediante métodos de imagen endovascular; sin embargo, estos métodos son subutilizados en la práctica clínica contemporánea y enfrentan desafíos en la interpretación de los datos obtenidos, por lo que la integración de la inteligencia artificial (IA) se vislumbra como una solución atractiva para promover y simplificar su uso. La IA se define como un programa computarizado que imita la capacidad del cerebro humano para recopilar y procesar datos. El aprendizaje de máquinas es una subdisciplina de la IA que implica la creación de algoritmos capaces de analizar grandes conjuntos de datos sin suposiciones previas, mientras que el aprendizaje profundo se centra en la construcción y entrenamiento de redes neuronales artificiales profundas y complejas. Así, se ha demostrado que la incorporación de sistemas de IA a los métodos de imagen endovascular incrementa la precisión de la ATC, disminuye el tiempo del procedimiento y la variabilidad interobservador en la interpretación de los datos obtenidos, promueve así una mayor adopción y facilita su utilización. El propósito de la presente revisión es destacar cómo los sistemas actuales basados en IA pueden desempeñar un papel fundamental en la interpretación de los datos generados por los métodos de imagen endovascular, lo que conduce a una mejora en la optimización de la ATC en pacientes con ECA.

ABSTRACT Percutaneous coronary intervention (PCI) is one of the primary revascularization strategies in patients with coronary artery disease (CAD). Several studies support the use of intravascular imaging methods to optimize PCI. However, these methods are underutilized in contemporary clinical practice and face challenges in data interpretation. Therefore, the incorporation of artificial intelligence (AI) is seen as an attractive solution to promote and simplify their use. AI can be defined as a computer program that mimics the human brain in its ability to collect and process data. Machine learning is a sub-discipline of AI that involves the creation of algorithms capable of analyzing large datasets without making prior assumptions, while deep learning focuses on the construction and training of deep and complex artificial neural networks. The incorporation of AI systems to intravascular imaging methods improves the accuracy of PCI, reduces procedure duration, and minimizes interobserver variability in data interpretation. This promotes their wider adoption and facilitates their use. The aim of this review is to highlight how current AI-based systems can play a key role in the interpretation of data generated by intravascular imaging methods and optimize PCI in patients with CAD.

Inteligencia artificial. Aplicación en las imágenes cardiovasculares y la prevención cardiovascular / Artificial Intelligence in Cardiovascular Imaging and Cardiovascular Prevention

RESUMEN La inteligencia artificial (IA) está basada en programas computacionales que pueden imitar el pensamiento humano y automatizar algunos procesos. En el ámbito médico se está estudiando hace más de 50 años, pero en los últimos años el crecimiento ha sido exponencial. El campo de las imágenes cardiovasculares es particularmente atractivo para aplicarla, dado que, guiadas por IA, personas no expertas pueden adquirir imágenes completas, automatizar procesos y mediciones, orientar diagnósticos, detectar hallazgos no visibles al ojo humano, realizar diagnósticos oportunistas de afecciones no buscadas en el estudio índice pero evaluables a través de las imágenes disponibles, o identificar patrones de asociación dentro de una gran cantidad de datos como fuente de generación de hipótesis. En el campo de la prevención cardiovascular, la IA se ha aplicado en diferentes escenarios con fines diagnósticos, pronósticos y terapéuticos en el manejo de algunos factores de riesgo cardiovascular, como las dislipidemias o la hipertensión arterial. Si bien existen limitaciones con el uso de la IA tales como el costo, la accesibilidad y la compatibilidad de los programas, la validez externa de los resultados en determinadas poblaciones, o algunos aspectos éticos-legales (privacidad de los datos), esta tecnología está en crecimiento vertiginoso y posiblemente revolucione la práctica médica actual.

ABSTRACT Artificial intelligence (AI) is based on computer programs that imitate human thinking and automate certain processes. Artificial intelligence has been studied in the medical field for over 50 years, but in recent years, its growth has been exponential. The field of cardiovascular imaging is particularly attractive since AI can guide non-experts in image acquisition, automate processes and measurements, guide diagnoses, detect findings not visible to the human eye, make opportunistic diagnoses of unexpected conditions in the index test, or identify patterns of association within a large amount of data as a source of hypothesis generation. In the field of cardiovascular prevention, AI has been used for diagnostic, prognostic, and therapeutic purposes in managing cardiovascular risk factors such as dyslipidemia and hypertension. While there are limitations to the use of AI, such as cost, accessibility, compatibility of programs, external validity of results in certain populations, and ethical-legal aspects such as data privacy, this technology is rapidly growing and is likely to revolutionize current medical practice.

ChatGPT's learning and reasoning capacity in anesthesiology

Introduction: Over the past few months, ChatGPT has raised a lot of interest given its ability to perform complex tasks through natural language and conversation. However, its use in clinical decision-making is limited and its application in the field of anesthesiology is unknown. Objective: To assess ChatGPT's basic and clinical reasoning and its learning ability in a performance test on general and specific anesthesia topics. Methods: A three-phase assessment was conducted. Basic knowledge of anesthesia was assessed in the first phase, followed by a review of difficult airway management and, finally, measurement of decision-making ability in ten clinical cases. The second and the third phases were conducted before and after feeding ChatGPT with the 2022 guidelines of the American Society of Anesthesiologists on difficult airway management. Results: On average, ChatGPT succeded 65% of the time in the first phase and 48% of the time in the second phase. Agreement in clinical cases was 20%, with 90% relevance and 10% error rate. After learning, ChatGPT improved in the second phase, and was correct 59% of the time, with agreement in clinical cases also increasing to 40%. Conclusions: ChatGPT showed acceptable accuracy in the basic knowledge test, high relevance in the management of specific difficult airway clinical cases, and the ability to improve after learning.

Introducción: En los últimos meses, ChatGPT ha suscitado un gran interés debido a su capacidad para realizar tareas complejas a través del lenguaje natural y la conversación. Sin embargo, su uso en la toma de decisiones clínicas es limitado y su aplicación en el campo de anestesiología es desconocido. Objetivo: Evaluar el razonamiento básico, clínico y la capacidad de aprendizaje de ChatGPT en una prueba de rendimiento sobre temas generales y específicos de anestesiología. Métodos: Se llevó a cabo una evaluación dividida en tres fases. Se valoraron conocimientos básicos de anestesiología en la primera fase, seguida de una revisión del manejo de vía aérea difícil y, finalmente, se midió la toma de decisiones en diez casos clínicos. La segunda y tercera fases se realizaron antes y después de alimentar a ChatGPT con las guías de la Sociedad Americana de Anestesiólogos del manejo de la vía aérea difícil del 2022. Resultados: ChatGPT obtuvo una tasa de acierto promedio del 65 % en la primera fase y del 48 % en la segunda fase. En los casos clínicos, obtuvo una concordancia del 20 %, una relevancia del 90 % y una tasa de error del 10 %. Posterior al aprendizaje, ChatGPT mejoró su tasa de acierto al 59 % en la segunda fase y aumentó la concordancia al 40 % en los casos clínicos. Conclusiones: ChatGPT demostró una precisión aceptable en la prueba de conocimientos básicos, una alta relevancia en el manejo de los casos clínicos específicos de vía aérea difícil y la capacidad de mejoría secundaria a un aprendizaje.

Artificial intelligence, applications and challenges in simulation-based education

The rapid advancement of Artificial Intelligence (AI) has taken the world by "surprise" due to the lack of regulation over this technological innovation which, while promising application opportunities in different fields of knowledge, including education, simultaneously generates concern, rejection and even fear. In the field of Health Sciences Education, clinical simulation has transformed educational practice; however, its formal insertion is still heterogeneous, and we are now facing a new technological revolution where AI has the potential to transform the way we conceive its application.

El rápido avance de la inteligencia artificial (IA) ha tomado al mundo por "sorpresa" debido a la falta de regulación sobre esta innovación tecnológica, que si bien promete oportunidades de aplicación en diferentes campos del conocimiento, incluido el educativo, también genera preocupación e incluso miedo y rechazo. En el campo de la Educación en Ciencias de la Salud la Simulación Clínica ha transformado la práctica educativa; sin embargo, aún es heterogénea su inserción formal, y ahora nos enfrentamos a una nueva revolución tecnológica, en la que las IA tienen el potencial de transformar la manera en que concebimos su aplicación.

Una revisión sistemática del uso de la inteligencia artificial en la educación / A systematic review of the use of artificial intelligence in education

Introducción. El uso de la inteligencia artificial (IA) en la educación ha sido objeto de una creciente atención en los últimos años. La IA se ha utilizado para mejorar la personalización del aprendizaje, la retroalimentación y la evaluación de los estudiantes. Sin embargo, también hay desafíos y limitaciones asociados. El objetivo de este trabajo fue identificar las principales tendencias y áreas de aplicación de la inteligencia artificial en la educación, así como analizar los beneficios y limitaciones de su uso en este ámbito. Métodos. Se llevó a cabo una revisión sistemática que exploró el empleo de la inteligencia artificial en el ámbito educativo. Esta revisión siguió una metodología de investigación basada en la búsqueda de literatura, compuesta por cinco etapas. La investigación se realizó utilizando Scopus como fuente de consulta primaria y se empleó la herramienta VOSviewer para analizar los resultados obtenidos. Resultados. Se encontraron numerosos estudios que investigan el uso de la IA en la educación. Los resultados sugieren que la IA puede mejorar significativamente la personalización del aprendizaje, proporcionando recomendaciones de actividades y retroalimentación adaptadas a las necesidades individuales de cada estudiante. Conclusiones. A pesar de las ventajas del uso de la IA en la educación, también hay desafíos y limitaciones que deben abordarse, como la calidad de los datos utilizados por la IA, la necesidad de capacitación para educadores y estudiantes, y las preocupaciones sobre la privacidad y la seguridad de los datos de los estudiantes. Es importante seguir evaluando los efectos del uso de la IA en la educación para garantizar su uso efectivo y responsable.

Introduction. The use of artificial intelligence (AI) in education has been the subject of increasing attention in recent years. AI has been used to improve personalized learning, feedback, and student assessment. However, there are also challenges and limitations. The aim of this study was to identify the main trends and areas of application of artificial intelligence in education, as well as to analyze the benefits and limitations of its use in this field. Methods. A systematic review was carried out on the use of artificial intelligence in education, using a literature search research methodology with five stages, based on the Scopus query and the tool for analyzing results with VOSviewer. Results. Numerous studies investigating the use of AI in education were found. The results suggest that AI can significantly improve personalized learning by providing activity recommendations and feedback tailored to the individual needs of each student. Conclusions. Despite the advantages of using AI in education, there are also challenges and limitations that need to be addressed, such as the quality of data used by AI, the need for training for educators and students, and concerns about the privacy and security of student data. It is important to continue evaluating the effects of AI use in education to ensure its effective and responsible use.

Application progress of artificial intelligence in the diagnosis of esophageal cancer / 中国胸心血管外科临床杂志

@#Esophageal cancer is an aggressive malignancy with high morbidity and poor prognosis. Symptoms of early esophageal cancer are insidious and difficult to detect, while advanced esophageal obstruction, lesion infiltration and metastasis seriously affect patients’ quality of life. Early detection and treatment can help to increase the survival chance of patients. Recently, artificial intelligence (AI) has shown remarkable success in diagnosis of esophageal cancer, highlighting the great potential of new AI-assisted diagnostic modalities. This paper aims to review recent progress of AI in the diagnosis of esophageal cancer and to prospect its clinical application.

Research progress of artificial intelligence in pathological subtypes classification and gene expression analysis of lung adenocarcinoma / 中国胸心血管外科临床杂志

@#Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.

Methods for Identifying Seeds of Chinese Medicinal Materials： A Review / 中国实验方剂学杂志

Seeds are the source for the production of Chinese medicinal materials. The seed authenticity and quality of directly affect the effectiveness and safety of Chinese medicinal materials. The seed quality is faced with the problems such as mixed sources， existence of adulterants and seeds stocked for years， low maturity， and low purity. To ensure the high-quality and sustainable development of the Chinese medicinal material industry， it is urgent to standardize the seed market and identify and evaluate the quality of the seeds circulating in the market. Seed identification methods include visual inspection， microscopic observation， micro-character identification， chemical fingerprinting， molecular identification， electronic nose， X-ray diffraction， electrochemical fingerprinting， spectral imaging， and artificial intelligence. These methods have different application scopes and unique advantages and disadvantages. According to the different species of Chinese herbal medicines and different requirements of testing sites， suitable methods can be selected to achieve rapid and accurate identification with low costs. In the future， the seed identification methods should be developed based on emerging technologies with interdisciplinary knowledge， and intelligent， nondestructive， and single-grain detection methods are needed for the modern Chinese medicinal material industry. This paper introduces the seed identification technologies currently applied in research and production， compares the principles， applicability， advantages， and disadvantages of different technologies， and provides an outlook on the future development of seed identification technologies， aiming to provide a reference for the identification and quality evaluation of seeds of Chinese medicinal material.

The Development of Chinese Herbal Formulae for Non-severe COVID-19 Based on Artificial Intelligence Technology and Investigation of Its Action Mechanisms / 中医杂志

ObjectiveTo develop traditional Chinese medicine （TCM） formulae for the treatment of nonsevere coronavirus disease 2019 （COVID-19） and to explore its anti-inflammatory mechanism. MethodsThe dysregulated signaling pathways were determined in macrophages from bronchoalveolar lavage fluid of COVID-19 patients and in lung epithelial cells infected with SARS-CoV-2 in vitro based on transcriptome analysis. A total of 102 TCM formulae for the clinical treatment of nonsevere COVID-19 were collected through literature. The pathway-reversing rates of these formulae in macrophages and lung epithelial cells were evaluated based on signature signaling pathways， and the basic formula was determined in conjunction with TCM theory. The commonly used Chinese materia medica for nonsevere COVID-19 were summarized from the 102 TCM formulae as abovementioned. And together with the screening results from the Pharmacopoeia of the People's Republic of China， a “Chinese materia medica pool” was esta-blished for the development of TCM formulae for COVID-19. The regulatory effects of each herb on signaling pathways were obtained based on targeted transcriptome analysis. Oriented at reversing dysregulated signaling pathways of COVID-19， the calculation was carried out， and the artificial intelligent methods for compositing formulae， that are exhaustive method and parallel computing， were used to obtain candidate compound formulas. Finally， with reference to professional experience， an innovative formula for the treatment of nonsevere COVID-19 was developed. The ethanol extract of the formula was evaluated for its anti-inflammatory effects by detecting the mRNA expression of interleukin 1b （Il1b）， C-X-C motif chemokine ligand 2 （Cxcl2）， C-X-C motif chemokine ligand 10 （Cxcl10）， C-C motif chemokine ligand 2 （Ccl2）， nitric oxide synthase 2 （Nos2）， and prostaglandin-endoperoxide synthase 2 （Ptgs2） using reverse transcription-quantitative polymerase chain reaction （RT-qPCR） in RAW264.7 cells treated with lipopolysaccharide （LPS）. ResultsIn macrophages and lung epithelial cells， 34 dysregulated signaling pathways associated with COVID-19 were identified respectively. The effects of the 102 formulae for clinical treatment of nonsevere COVID-19 were evaluated based on the dysregulated signaling pathways and targeted transcriptome， and the result showed that Yinqiao Powder and Pingwei Powder （银翘散合平胃散， YQPWP） ranked first， reversing 91.18% of the dysregulated signaling pathways in macrophages and 100% of the dysregulated signaling pathways in lung epithelial cells. Additionally， YQPWP had the function of scattering wind and clearing heat， resolving toxins and removing dampness in accordance with the pathogenesis of wind-heat with dampness in COVID-19. It was selected as the basic formula， and was further modified and optimized to develop an innovative fomula Qiaobang Zhupi Yin （翘蒡术皮饮， QBZPY） based on expert experience and artificial intelligence in composing formulae. QBZPY can reverse all the dysregulated signaling pathways associated with COVID-19 in macrophages and lung epithelial cells， with the reversing rates of 100%. The chief medicinal of QBZPY， including Lianqiao （Fructus Forsythiae）， Xixiancao （Herba Siegesbeckiae） and Niubangzi （Fructus Arctii）， can down-regulate multiple signaling pathways related with virus infection， immune response， and epithelial damage. RT-qPCR results indicated that compared with the model group， the QBZPY group down-regulated the mRNA expression of Il1b， tumor necrosis factor （Tnf）， Cxcl2， Cxcl10， Ccl2， Nos2 and Ptgs2 induced by LPS in RAW264.7 cells （P＜0.05 or P＜0.01）. ConclusionBased on targeted transcriptome analysis， expert experience in TCM and artificial intelligence， QBZPY has been developed for the treatment of nonsevere COVID-19. The ethanol extract of QBZPY has been found to inhibit mRNA expression of several pro-inflammatory genes in a cellular inflammation model.

Artificial intelligence ChatGPT in ophthalmology and scientific writing / 国际眼科杂志(Guoji Yanke Zazhi)

ChatGPT is a large language models(LLMs)that uses deep learning techniques to produce human-like responses to natural language inputs. It belongs to the family of generative pre-training transformer(GPT)models currently publicly available developed by OpenAI in November 2022. ChatGPT is capable of capturing the nuances and intricacies of human language, generating appropriate and contextually relevant responses. It can assist medical professionals in various tasks, such as research, diagnosis, patient monitoring, and medical education, from identifying research programs to assisting in clinical and laboratory diagnosis, to know new developments in their fields and scientific writing. ChatGPT has also attracted increasing attention and widely used in ophthalmology. However, the use of ChatGPT and other artificial intelligence tools in such tasks comes now with several limitations, ethical and legal concerns, such as credibility, plagiarism, copyright infringement, and biases. Future research can focus on developing new methods to mitigate these limitations while harnessing the benefits of ChatGPT in medicine and related aspects.

Application status of artificial intelligence techniques in retinoblastoma / 国际眼科杂志(Guoji Yanke Zazhi)

Retinoblastoma is a kind of malignant eye tumor commonly seen in children, which is one of the main causes threatening children's vision and life. The diagnosis and evaluation of retinoblastoma has always been a hot topic in clinic. In the past few years, the application of artificial intelligence(AI)technology has made significant progress in the medical field, providing new opportunities and challenges for the diagnosis and treatment of retinoblastoma, for example, the use of AI algorithms to analyze massive clinical data, which can help doctors diagnose the disease more accurately and provide personalized treatment plans. In addition, AI technology also plays an important role in medical image analysis, genomics research and other aspects, which can help the development of new drugs and improve patient prognosis. This article reviews the application progress of AI in retinoblastoma.

Challenges of computer-assisted orthognathic surgery in clinical application / 口腔疾病防治

@#After years of development, the advantages of computer-assisted orthognathic surgery have been widely recognized. However, the clinical application of this technology is challenging. Each step may generate errors from data acquisition, computer-assisted diagnosis, and computer-assisted surgical design, causing errors to be transferred from the virtual surgical plan to the operation. The accumulation and amplification of errors will affect the final surgical effect. Currently, digital devices, such as intraoral scanners, are being explored for error control, utilizing automation methods and algorithms, and implementing personalized bone positioning methods. Moreover, there are still many problems that have not been fully resolved, such as precise simulation of postoperative soft tissue, functional assessment of mandibular movement, and absorbable internal fixation materials. Fully understanding computer-assisted orthognathic surgery's limitations could provide direction for optimizing existing methods while helping clinicians avoid risks and maximize its advantages to achieve the best outcome. Many emerging and cutting-edge technologies, such as personalized titanium plates, artificial intelligence, and surgical robots, will further promote the development of this discipline. We can expect future optimization of digital orthognathic surgical technology by innovations in automation, intelligence, and personalization.

Advances in the differential diagnosis of malignant bile duct stenosis / 临床肝胆病杂志

There are various etiologies for extrahepatic bile duct stenosis， and pharmacotherapy and endoscopic intervention can achieve a good clinical effect in benign stenosis. Early diagnosis and timely surgical treatment of malignant stenosis may prolong the survival time of patients. However， there are still difficulties in the differential diagnosis of malignant bile duct stenosis. This article reviews the application of serology， radiology， endoscopic techniques， and artificial intelligence in the differential diagnosis of malignant bile duct stenosis， so as to provide strategies and references for formulating clinical diagnosis and treatment regimens.

Research progress on cardiovascular hemodynamic assessment based on computational fluid dynamics / 中国胸心血管外科临床杂志

@#Hemodynamics plays a vital role in the development and progression of cardiovascular diseases, and is closely associated with changes in morphology and function. Reliable detection of hemodynamic changes is essential to improve treatment strategies and enhance patient prognosis. The combination of computational fluid dynamics with cardiovascular imaging technology has extended the accessibility of hemodynamics. This review provides a comprehensive summary of recent developments in the application of computational fluid dynamics for cardiovascular hemodynamic assessment and a succinct discussion for potential future development.

Application of artificial intelligence in ophthalmic plastic surgery / 国际眼科杂志(Guoji Yanke Zazhi)

The advancement of computers and data explosion have ushered in the third wave of artificial intelligence(AI). AI is an interdisciplinary field that encompasses new ideas, new theories, and new technologies, etc. AI has brought convenience to ophthalmology application and promoted its intelligent, precise, and minimally invasive development. At present, AI has been widely applied in various fields of ophthalmology, especially in oculoplastic surgery. AI has made rapid progress in image detection, facial recognition, etc., and its performance and accuracy have even surpassed humans in some aspects. This article reviews the relevant research and applications of AI in oculoplastic surgery, including ptosis, single eyelid, pouch, eyelid mass, and exophthalmos, and discusses the challenges and opportunities faced by AI in oculoplastic surgery, and provides prospects for its future development, aiming to provide new ideas for the development of AI in oculoplastic surgery.