Artificial Intelligence Science Research Articles

China and the U.S. produce more impactful AI research when collaborating together

Artificial Intelligence (AI) has become a disruptive technology, promising to grant a significant economic and strategic advantage to nations that harness its power. China, with its recent push towards AI adoption, is challenging the U.S.’s position as the global leader in this field. Given AI’s massive potential, as well as the fierce geopolitical tensions between China and the U.S., several recent policies have been put in place to discourage AI scientists from migrating to, or collaborating with, the other nation. Nevertheless, the extent of talent migration and cross-border collaboration are not fully understood. Here, we analyze a dataset of over 350,000 AI scientists and 5,000,000 AI papers. We find that since 2000, China and the U.S. have led the field in terms of impact, novelty, productivity, and workforce. Most AI scientists who move to China come from the U.S., and most who move to the U.S. come from China, highlighting a notable bidirectional talent migration. Moreover, the vast majority of those moving in either direction have Asian ancestry. Upon moving, those scientists continue to collaborate frequently with those in the origin country. Although the number of collaborations between the two countries has increased since the dawn of the millennium, such collaborations continue to be relatively rare. A matching experiment reveals that the two countries have always been more impactful when collaborating than when each works without the other. These findings suggest that instead of suppressing cross-border migration and collaboration between the two nations, the science could benefit from promoting such activities.

Эмотикон как пример развития графической эмотиологии в виртуальном пространстве

Emoticons are a vivid example of graphic emotionology in virtual communication. The interdisciplinary character of this research made it possible to combine linguistics with exact and natural sciences, including the young science of emotional artificial intelligence. Transmitting psycho-emotional experiences is important for effective virtual communication. Emotional pictography provides a more accurate understanding of the emotive content in a message. It relies on associative links between the image of a specific psycho-emotional state and its perception by a carrier of a specific linguistic culture. Basic emoticons are of universal nature, and internet users with different cultural and linguistic backgrounds tend to interpret them similarly. However, members of different ethno-linguistic communities sometimes decode the same emotions differently, which may lead to misunderstanding, as well as to serve as a marker on the friend – foe axis. Emoticons represent a clearly verified range of emotive states, which can be appealed to in virtual communication. The authors developed a classification of emoticons and proposed graphic emotionology as a new branch of linguistic semiotics. The metaphorical nature of the emoticon as a coded emotion with additional evaluative shades is revealed as a certain unified image or a complex of signs due to the accepted emotive norms of speech behavior. In addition, emoticons provide efficient compression during virtual communication. Intercultural virtual communication enriches the range of emoticons both linguistically and culturally, thus developing graphic emotionology internationally. Emoticons provide insight into the development of graphic emotionology.

AI meets physics: a comprehensive survey

Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence (AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide range of matter, energy, and space-time scales through data, knowledge, priors, and laws. At the same time, the AI paradigm also draws on and introduces the knowledge and laws of physics to promote its own development. Then this new paradigm of using physical science to inspire AI is the physical science of artificial intelligence (PhysicsScience4AI, PS4AI). Although AI has become the driving force for development in various fields, there is still a “black box” phenomenon that is difficult to explain in the field of AI deep learning. This article will briefly review the connection between relevant physics disciplines (classical mechanics, electromagnetism, statistical physics, quantum mechanics) and AI. It will focus on discussing the mechanisms of physics disciplines and how they inspire the AI deep learning paradigm, and briefly introduce some related work on how AI solves physics problems. PS4AI is a new research field. At the end of the article, we summarize the challenges facing the new physics-inspired AI paradigm and look forward to the next generation of artificial intelligence technology. This article aims to provide a brief review of research related to physics-inspired AI deep algorithms and to stimulate future research and exploration by elucidating recent advances in physics.

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Keywords: Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.

A Framework Design for Centralised Monitoring of Patient Disease Diagnosis for Better Improvement

Healthcare recommendation systems have garnered significant attention in recent times due to their capacity to improve patient outcomes and treatment. This literature review intends to assess the current state of patient healthcare referral systems by examining relevant studies, techniques, and findings. The report focuses on key research areas, challenges, and viable strategies for the future in the field of patient-centered health recommendation systems. Currently, healthcare administration is in high demand due to its significant advantages in managing hospitals or medical practices. Health management systems are increasingly affecting the entire world on a daily basis. The rising demand for healthcare is attributed to various factors, including the availability of healthcare solutions. The health prediction system is an online initiative designed to provide user support and advice. This study proposes a technology that allows consumers to receive immediate online health guidance from an intelligent healthcare system. The system encompasses a multitude of disorders and symptoms associated with different bodily systems. Data mining technologies can be utilized to identify the most probable disease associated with a patient's symptoms. By logging into the system, a doctor can retrieve and review their patient's information and reports within the doctor's module. Physicians have the ability to analyze the patient's browsing history and the specific information they are seeking, taking into account their medical prognosis. The doctor has access to his data. The database administrator has the ability to incorporate additional disease information, such as the type of disease and its symptoms. The data mining system runs based on the condition's name and symptoms. The administrator has access to the database including information on diseases and symptoms. Recommender systems employ diverse machine learning techniques in many domains, such as the healthcare recommendation system (HRS), to advise and promote services or entities to users. Due to the vast array of algorithms documented in the literature, the science of artificial intelligence is now widely employing machine learning techniques in various application domains, including the HRS. Nevertheless, the process of selecting an appropriate machine learning algorithm for a health recommender system seems to be time-consuming.

Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.

The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.

Artificial Intelligence in Science: on the Threshold of a New Field of Knowledge?

Artificial Intelligence in Science: on the Threshold of a New Field of Knowledge?

The Science of Artificial Intelligence

The Science of Artificial Intelligence

Artificial Intelligence: A forward-looking vision for a better future for humanity Analytical theoretical study

The world is witnessing a tremendous Revolution in terms of Technological Development, entry into the world of Artificial Intelligence, and the possession of modern technologies in automation and algorithms that are useful for computers and robots, which is certainly necessary and indispensable, as; Progress and development in various fields require the necessity of relying on artificial intelligence technologies and its multiple uses. Despite the advantages of using artificial intelligence, it faces many challenges and risks that face humans as a result of these uses. They are not limited to the various aspects (economic, social, and media) that affect humans from Where only the labor market and unemployment rates, but there are also other risks according to the nature and specificity of the social and cultural context of our Arab societies that must be taken into account when entering the world of artificial intelligence? All of these reasons made the researcher address the research issue and the current research topic This Research aims to Learn the Science of Artificial Intelligence, and provide a forward-looking vision of what a better future for humanity may look like in the future. This is because it is a modern Science that has gained great Importance in recent years for its many applications in various fields. It also aims to identify its importance, benefits, types and concepts associated with it, and reveal the areas of use of Artificial Intelligence applications, how to use them, and the challenges facing its use. This research was devoted to studying the reality of artificial intelligence. In light of the developments to identify the challenges and address them automatically, to develop a future vision about artificial intelligence in the twenty-first century, the research was entitled: Artificial Intelligence: A forward-looking vision for a better future for humanity Keywords:) A Proposed Vision - Artificial Intelligence - Forward-looking vision - Applications of Artificial Intelligence – Technology (

Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms.

Myocardial infarction (MI) is a major cause of death, particularly during the first year. The avoidance of potentially fatal outcomes requires expeditious preventative steps. Machine learning (ML) is a subfield of artificial intelligence science that detects the underlying patterns of available big data for modeling them. This study aimed to establish an ML model with numerous features to predict the fatal complications of MI during the first 72 hours of hospital admission. We applied an MI complications database that contains the demographic and clinical records of patients during the 3 days of admission based on 2 output classes: dead due to the known complications of MI and alive. We utilized the recursive feature elimination (RFE) method to apply feature selection. Thus, after applying this method, we reduced the number of features to 50. The performance of 4 common ML classifier algorithms, namely logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost), was evaluated using 8 classification metrics (sensitivity, specificity, precision, false-positive rate, false-negative rate, accuracy, F1-score, and AUC). In this study of 1699 patients with confirmed MI, 15.94% experienced fatal complications, and the rest remained alive. The XGBoost model achieved more desirable results based on the accuracy and F1-score metrics and distinguished patients with fatal complications from surviving ones (AUC=78.65%, sensitivity=94.35%, accuracy=91.47%, and F1-score=95.14%). Cardiogenic shock was the most significant feature influencing the prediction of the XGBoost algorithm. XGBoost algorithms can be a promising model for predicting fatal complications following MI.

Brain Tumor Detection with Deep Learning Methods’ Classifier Optimization Using Medical Images

It is known that, with the development of artificial intelligence science in recent years, it has started to be used in all areas of life. Due to the increase in diseases that threaten human life, such as epidemics and cancer, more attention has been paid to research in this field. Especially in the field of biomedical image processing, very successful results have been obtained in recent years with the use of deep learning methods. For this study, MR images are utilized to diagnose brain tumors. To assist doctors and radiologists in automatic brain tumor diagnosis and to overcome the need for manual diagnosis, a brain MR image automated classification system is being developed. The data used in the study are open access data obtained from the Kaggle library. This paper presents a novel approach for classifying brain MR images utilizing a dataset of 7022 MR images. To give an unbiased evaluation of the dataset, it is divided into a 40% test and 60% training set. Respectively, VGG, ResNet, DenseNet and SqueezeNet architectures are trained and used for feature extraction from brain MRI images. In order to classify the extracted features, machine learning methods (Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Decision Tree, Linear Regression Analysis) are applied first, then an ensemble learning method is applied and the best validation method is selected. In addition, parameter optimization is applied to the trained CNN algorithms. In order to develop the proposed methods, the Python software program was used in the training and testing phases of the models, and the classification success rates were mutually evaluated. Among the results found, it can see that the ResNet architecture reached 100% accuracy. The data obtained as a result of the study were compared with the results of similar studies. In conclusion, the techniques and methods applied highlight their effectiveness in accurately classifying brain MRI images and their potential to improve diagnostic capabilities.

Obesity Current Research and Future Hope

Obesity is an all-encompassing community health concern characterized by overconsumption of carcass fat, superior to adverse strength effects, containing cardiovascular ailments, diabetes, and various cancers. This abstract supports a short survey of current research styles and future prospects in the battle against obesity. Current research exertions are directed at understanding the complex interaction of historical, environmental, and behavioral determinants relating to corpulence. Genetic studies have identified abundant susceptibleness genes, peeling away the hereditary facets of corpulence. Meanwhile, material determinants such as obesogenic diets and lazy behaviors stretch to be key extents of investigation. Novel healing attacks and behavior qualification strategies are energetically being examined. Approaches involve the growth of anti-corpulence drugs, bariatric abscission, and attacks address gut micro biota. Promising findings are arising in the fields of accuracy, cure, personalized digestive approvals, plant structure, and individualized absorption. The future hope in the fight against corpulence lies in the unification of science and mathematical strength tools. Mobile devices, wearable devices, and telehealth manifestos offer creative ways to monitor and control corpulence. The big dossier science of logical analysis and artificial intelligence can help healthcare providers and cases form cognizant resolutions for personalized burden administration.

Virtual laboratories: transforming research with AI

Abstract New scientific knowledge is needed more urgently than ever, to address global challenges such as climate change, sustainability, health, and societal well-being. Could artificial intelligence (AI) accelerate science to meet these global challenges in time? AI is already revolutionizing individual scientific disciplines, but we argue here that it could be more holistic and encompassing. We introduce the concept of virtual laboratories as a new perspective on scientific knowledge generation and a means to incentivize new AI research and development. Despite the often perceived domain-specific research practices and inherent tacit knowledge, we argue that many elements of the research process recur across scientific domains and that even common software platforms for serving different domains may be possible. We outline how virtual laboratories will make it easier for AI researchers to contribute to a broad range of scientific domains, and highlight the mutual benefits virtual laboratories offer to both AI and domain scientists.

Semantic features of complex technosocial systems: On the taxonomy of artificial intelligence technological packages

Objectives. The aim ofthis work is to enhance the scientific and methodological apparatus of artificial intelligence (AI) sciences by enriching their conceptual framework. The current conceptual framework of AI sciences does not reflect the intricate nature of this technological and socioeconomic phenomenon as possessing the diverse range of capabilities and the interconnectedness that allows for the imitation of human cognitive functions and comparable results. The author of the article structures the concept of the technological package of AI, describing its system properties, connections and functional elements based on the various types of human cognitive and operational activities.Methods. The research is based on the concept (method) of technological packages—genetically and functionally connected sets of technologies with system properties.Results. For the first time in Russian and international practice, the basic (general) taxonomy of the AI technological package has been specified and structured. A taxonomy of the AI metatechnological package (a package of metatechnologies) has been proposed. General taxonomy can serve as a tool for improving strategies, methodological documents and state programs to define the development of AI systems at state or industry level.Conclusions. The suggested basic (general) taxonomy oftechnological package and taxonomy of metatechnologies package allows research to move away from the limited view of AI. It increases semantic and methodological clarity in relation to AI as a complex technosocial phenomenon and contributes to the harmonized integration of AI systems intо the sphere of socioeconomic activities of the state. It can thus serve as a foundation for further improvement of state economic and legal regulation of AI development.

Digital Discovery of 100 diverse Quantum Experiments with PyTheus

Photons are the physical system of choice for performing experimental tests of the foundations of quantum mechanics. Furthermore, photonic quantum technology is a main player in the second quantum revolution, promising the development of better sensors, secure communications, and quantum-enhanced computation. These endeavors require generating specific quantum states or efficiently performing quantum tasks. The design of the corresponding optical experiments was historically powered by human creativity but is recently being automated with advanced computer algorithms and artificial intelligence. While several computer-designed experiments have been experimentally realized, this approach has not yet been widely adopted by the broader photonic quantum optics community. The main roadblocks consist of most systems being closed-source, inefficient, or targeted to very specific use-cases that are difficult to generalize. Here, we overcome these problems with a highly-efficient, open-source digital discovery framework PyTheus, which can employ a wide range of experimental devices from modern quantum labs to solve various tasks. This includes the discovery of highly entangled quantum states, quantum measurement schemes, quantum communication protocols, multi-particle quantum gates, as well as the optimization of continuous and discrete properties of quantum experiments or quantum states. PyTheus produces interpretable designs for complex experimental problems which human researchers can often readily conceptualize. PyTheus is an example of a powerful framework that can lead to scientific discoveries – one of the core goals of artificial intelligence in science. We hope it will help accelerate the development of quantum optics and provide new ideas in quantum hardware and technology.

Can ChatGPT help patients answer their otolaryngology questions?

Over the past year, the world has been captivated by the potential of artificial intelligence (AI). The appetite for AI in science, specifically healthcare is huge. It is imperative to understand the credibility of large language models in assisting the public in medical queries. To evaluate the ability of ChatGPT to provide reasonably accurate answers to public queries within the domain of Otolaryngology. Two board-certified otolaryngologists (HZ, RS) inputted 30 text-based patient queries into the ChatGPT-3.5 model. ChatGPT responses were rated by physicians on a scale (accurate, partially accurate, incorrect), while a similar 3-point scale involving confidence was given to layperson reviewers. Demographic data involving gender and education level was recorded for the public reviewers. Inter-rater agreement percentage was based on binomial distribution for calculating the 95% confidence intervals and performing significance tests. Statistical significance was defined as p < .05 for two-sided tests. In testing patient queries, both Otolaryngology physicians found that ChatGPT answered 98.3% of questions correctly, but only 79.8% (range 51.7%-100%) of patients were confident that the AI model was accurate in its responses (corrected agreement = 0.682; p < .001). Among the layperson responses, the corrected coefficient was of moderate agreement (0.571; p < .001). No correlation was noted among age, gender, or education level for the layperson responses. ChatGPT is highly accurate in responding to questions posed by the public with regards to Otolaryngology from a physician standpoint. Public reviewers were not fully confident in believing the AI model, with subjective concerns related to less trust in AI answers compared to physician explanation. Larger evaluations with a representative public sample and broader medical questions should immediately be conducted by appropriate organizations, governing bodies, and/or governmental agencies to instill public confidence in AI and ChatGPT as a medical resource. 4.

A Literature Review on the Effect of Artificial Intelligence on Education

The science of artificial intelligence has been advancing at a great pace in recent years. Artificial intelligence, one of the most important technologies of our age, will cause a great transformation for the world. Artificial intelligence has also affected education and training, leading to significant developments in the field of learning technologies and educational technology. Interest in computer systems and artificial intelligence is increasing and the studies on them are increasing. This article examines the issue of digital transformation in education as a result of the development of artificial intelligence, and the new educational processes that will occur as a result of the interaction of humans and intelligent machines. Therefore, the aim of this study is to understand the impact of artificial intelligence on education. For this purpose, firstly, information is given about what artificial intelligence is. Then, it is discussed whether machines show human-like behaviors or not. Finally, an analysis is made on how and in what way artificial intelligence contributes to education, what the advantages and disadvantages of artificial intelligence are, and which artificial intelligence applications can be used in education. The research was conducted with a literature review from qualitative research methods. Artificial intelligence has provided students with enhanced learning experiences as it enables learning materials to be customized and personalized according to students' needs and abilities. AI in general has had a major impact on education, especially on the management, teaching and learning areas of the education sector or in the context of individual learning institutions.

Improving Automated Machine-Learning Systems through Green AI

Automated machine learning (AutoML), which aims to facilitate the design and optimization of machine-learning models with reduced human effort and expertise, is a research field with significant potential to drive the development of artificial intelligence in science and industry. However, AutoML also poses challenges due to its resource and energy consumption and environmental impact, aspects that have often been overlooked. This paper predominantly centers on the sustainability implications arising from computational processes within the realm of AutoML. Within this study, a proof of concept has been conducted using the widely adopted Scikit-learn library. Energy efficiency metrics have been employed to fine-tune hyperparameters in both Bayesian and random search strategies, with the goal of enhancing the environmental footprint. These findings suggest that AutoML can be rendered more sustainable by thoughtfully considering the energy efficiency of computational processes. The obtained results from the experimentation are promising and align with the framework of Green AI, a paradigm aiming to enhance the ecological footprint of the entire AutoML process. The most suitable proposal for the studied problem, guided by the proposed metrics, has been identified, with potential generalizability to other analogous problems.

Comprehensive study: machine learning approaches for COVID-19 diagnosis

&lt;p&gt;&lt;span lang="EN-US"&gt;Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods.&lt;/span&gt;&lt;/p&gt;

Identifikasi Tumor Otak Citra MRI dengan Convolutional Neural Network

The science of artificial intelligence and computer vision is beneficial in facilitating the detection of diseases in the medical field. Computer-based disease detection can save time. However, identifying and detecting tumors on MRI images require seriousness and is time-consuming. Due to the diversity of structures in size, shape, and intensity of the image, accuracy is needed in identifying the original organ structure and the diseased one. Previous studies have proposed a method for identifying brain tumors to produce the correct precision. In previous studies, neural network-based methods have good accuracy. We present five Convolutional Neural Network (CNN) architectures for identifying brain tumors (glioma, meningioma, no tumor, and pituitary) on MRI images. This study aims to develop an optimal CNN architecture for identifying tumors. We use the dataset from Kaggle with a total training data of 5712 and testing of 1311. Of the five proposed CNN architectures, architecture c has the highest accuracy of 82.2% with an unlimited number of parameters of 29605060. A good CNN architecture has many convolution layers. We also compare the proposed architecture with CNN transfer learning (Inception, ResNet-50, and VGG16), and with CNN transfer learning architecture, the accuracy is higher than our proposed architecture.