Artificial Intelligence Science Research Articles

Smart transplants: emerging role of nanotechnology and big data in kidney and islet transplantation, a frontier in precision medicine

Kidney and islet transplantation has revolutionized the management of renal failure and diabetes. Transplantation is considered as excellent therapeutic intervention for most suitable patients. While advancements in the surgical aspects, immunosuppression and outcomes have potentially plateaued, new technologies have developed which could enhance transplantation with benefits to patients and clinical teams alike. The science of nanotechnology and big data advancements are two such technologies, collectively paving the way for smarter transplantation solutions. Nanotechnology offers novel strategies to overcome critical challenges, including organ preservation, ischemia-reperfusion injury and immune modulation. Innovations such as nanoparticle-based drug delivery systems, biocompatible encapsulation technologies for islet transplants, and implantable artificial kidneys are redefining the standards of care. Meanwhile, big data analytics harness vast datasets to optimize donor-recipient matching, refine predictive models for post-transplant outcomes, and personalize therapeutic regimens. Integrating these technologies forms a synergistic framework where nanotechnology enhances therapeutic precision and big data provides actionable insights, enabling clinicians to adopt proactive, patient-specific strategies. By addressing unmet needs and leveraging the combined potential of nanotechnology and big data, this transformative approach promises to improve graft survival, functionality, and overall patient outcomes, marking a paradigm shift in transplantation medicine. These developments will also be accelerated with integration of the rapidly advancing science of artificial intelligence.

Development of a method for differential diagnosis of iron deficiency anemia and anemia of chronic disease based on demographic data and routine laboratory tests using machine learning technologies

Background. The study of machine learning methods, a branch of artificial intelligence science, is relevant for the development of optimal screening strategies, identification of risk groups, and application of less expensive and more accessible laboratory tests to assess the body iron status. Aim. To select an appropriate artificial intelligence algorithm for predicting serum ferritin (SF) levels and to evaluate its applicability for differential diagnosis of iron deficiency anemia and anemia of chronic diseases. Materials and methods. A dataset of 9771 patients with micro‑normocytic anemia was used to create the model. On the basis of demographic data (gender and age), clinical blood count, C‑reactive protein level and known SF level, a regression model was developed to calculate the expected SF concentration in a particular patient and, using the same parameters, a classification model to determine the SF level group to which the patient belongs: I – < 15 μg / L; II – 15–100 μg / L; III – 100–300 μg / L; Iv – ≥ 300 μg / L. Results. As a result, the regression model has moderate predictive ability (R2 = 0.70; median absolute error was 10.7 μg / L), the correlation coefficient between known and predicted SF level was r = 0.854 (p < 0.05). The obtained classification model has high diagnostic accuracy for different clinical groups according to the SF level (AuC ROC was 0.91; 0.79; 0.84; 0.90 and 0.96; 0.76; 0.71; 0.82 for patients with reduced hemoglobin levels in women (< 120 g / L) and men (< 130 g / L) in groups I, II, III, Iv, respectively). Conclusion. Prediction of SF level using the developed models can be used as an accurate and clinically relevant tool for differential diagnosis of iron deficiency anemia (predicted SF is decreased (< 100 μg / L), C‑reactive protein is normal) and anemia of chronic diseases (predicted SF is normal or increased (>100 μg / L), C‑reactive protein is increased) in real medical practice.

Introduction to the International Journal of Artificial Intelligence for Science (IJAI4S)

The International Journal of Artificial Intelligence for Science (IJAI4S) is established to address the growing role of artificial intelligence (AI) in scientific research. As AI technologies continue to advance, they are increasingly applied across various scientific domains, including physics, chemistry, biology, medicine, environmental science, and engineering. However, many traditional academic journals often categorize AI research strictly within computer science, overlooking its transformative impact on scientific discovery. IJAI4S aims to bridge this gap by providing a dedicated platform for high-quality interdisciplinary research that integrates AI with scientific challenges. This journal fosters innovation by accepting both applied AI research in scientific domains and fundamental AI algorithmic developments with scientific relevance. It upholds rigorous academic standards through a double-blind peer review system, ensuring contributions meet the highest levels of scientific integrity, reproducibility, and impact. As an open-access journal, IJAI4S is committed to broad knowledge dissemination, enabling researchers worldwide to access and build upon published findings freely. With a vision to become a leading journal in AI for Science, IJAI4S actively promotes international collaborations, organizes conferences and workshops, and recognizes outstanding contributions through annual best paper awards. By fostering a dynamic research community, the journal aims to accelerate AI-driven scientific advancements and shape the future of intelligent research methodologies. We invite scholars from diverse disciplines to submit their work and collaborate in driving forward the integration of AI in science.

Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management

Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence–based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.

Applications of artificial intelligence in science: a practical guide for editors and authors

Applications of artificial intelligence in science: a practical guide for editors and authors

Artificial Intelligence in Science and Mathematics Assessment for Students with Disabilities: Opportunities and Challenges

Emerging developments in artificial intelligence present significant opportunities to enhance equity and access to science and mathematics assessment content for students with disabilities. Artificial intelligence (AI) technologies may have the potential to support test developers in creating more inclusive assessments that better measure what students know and can do. But they must also consider the potential accessibility challenges or introduction of construct-irrelevant variance posed by these technologies. The purpose of this article is to provide a conceptual overview of the issues to be considered when creating and implementing large-scale science and mathematics assessments for students with disabilities. We discuss how AI has been utilized in large-scale assessments to date and describe the opportunities and potential pitfalls in the stages of the process: assessment design, development, administration, scoring, reporting, and data use. This article concludes with proposed priorities for research that will advance the responsible practice of AI in large-scale assessment that is inclusive, fair, and valid for students with disabilities. This article contributes to the growing body of information on AI applications for assessment by identifying the roles that AI can play in science and mathematics assessment practices and demonstrating how AI can inform approaches to equitable science, technology, engineering, and mathematics (STEM) learning.

ARTIFICIAL INTELLIGENCE IN SPORTS: A RETROSPECTIVE OF THE FORMATION AND INTEGRATION PROCESSES IN THE SPORTS INDUSTRY, INTERNATIONAL EXPERIENCE AND STATE OF THE ISSUE IN UKRAINE

In 2020, in Ukraine, the Concept for the development of piece intelligence in Ukraine was developed, prepared by the Ministry of Digital Transformation of Ukraine and approved by the Cabinet of Ministers of Ukraine 02.12.2020 No. 1556-r.. This itself acts as the main instrument for the development of integration processes of piece intelligence and all galuzites It is the main mechanism for its implementation, as a separate strategy. Sports science is also actively involved in this process. There are already dozens of applications for the effective development of artificial intelligence at all stages of sports training. However, the overall picture of research and applied aspects still contains a sufficiently fragmented and chaotic order, which will require an integrated scientific approach and the development of detailed algorithms for the integration of individual intelligence in sports activities and other sports activities. The purpose of the work was to look at the practical stagnation of the capabilities of artificial intelligence systems in sports. Find out and learn the necessary facts about artificial intelligence. Conduct a retrospective analysis of the research and development of human intelligence systems in sports in Ukraine and in the international arena. Research methods – experimental-theoretical (analysis, logical), empirical (development), theoretical (research and analysis, analysis and synthesis). Apparently, prior to the results of the investigation, a retrospective analysis of the formation and integration processes in sportswear, international evidence of the applied science of artificial intelligence in sports and nutrition in Ukraine was carried out.

Artificial Intelligence in Science and Society: The Vision of USERN

Artificial Intelligence in Science and Society: The Vision of USERN

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.

Leveraging Artificial Intelligence Tools & Techniques In Streamlining And Bolstering The Marketing Strategies For Attempting A Huge Business Expansion

The science of artificial intelligence (AI) is expanding quickly. It's the newest tech disruptor with huge potential for revolutionizing marketing. To identify the greatest AI solutions for their marketing departments, experts from all around the world are putting in a lot of effort. The world is widely regarded to be rapidly entering the digital era. The more prevalent use of digital content is confirmed by astute marketing companies that integrate AI technology into their campaigns. Artificial intelligence is being used by more companies to improve their offerings and streamline their processes. The current market structure is a result of advancements in neural network, deep learning, and machine learning technologies. An overview of e-marketing, sales and marketing, and artificial intelligence (AI) is provided in this paper. The use of artificial intelligence by marketers is then examined, along with the significance of using this approach to promote goods and services.