Artificial Intelligence in Reproductive Medicine

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers’ ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women’s nursing records and AI-based prediction of the risk of delivery according to pregnant women’s age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

Artificial intelligence usage in the communication field is of strategic importance. However, most communication practitioners and academicians have not noticed the gains in momentum substantially artificial intelligence. Through communication journals, this study aims to examine how artificial intelligence is used, its effects, and how important it is in communication. This study was designed as a content analysis and bibliometric analysis study. The sample of this study is AI-based articles in Web of Science Q1 indices communication journals. It selected and examined the first sixteen journals in communication keyword search based on SCImago Journal & Country Rank. This study revealed artificial intelligence usage in the communication industry and field, in addition to AI's impact and significance and AI-based technologies in communication. It was found that the most used method for artificial intelligence-based articles in communication journals was the semi-structured interview method. It is seen that common keywords in AI-based articles and communication journals are announced as human-machine communication, machine learning, artificial agent, artificial intelligence, bias, and social media concepts. In this study, a positive correlation between communication science and artificial intelligence technologies was observed. This study contributes to new studies on artificial intelligence, communication, the impacts of artificial intelligence on communication, new communication technologies, generative AI, human-machine communication, and other related areas.

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives

Artificial intelligence (AI) is in the era of rapid evolution. Like other healthcare fields, AI has significantly impacted sleep medicine. We aim to explain the evolving role of AI in sleep medicine and provide clinicians with key information related to its benefits and limitations. AI technologies, like machine learning and deep learning, improve the detection of sleep disorders, such as obstructive sleep apnea, insomnia, and narcolepsy, through advanced data analysis from tools like polysomnography and consumer sleep devices. AI also enables targeted therapies by endotyping sleep disorders, optimizing patient care, and reducing unnecessary treatments. On the other hand, there are many challenges that need to be addressed before using AI in clinical settings. Ethical issues regarding patient privacy, biases, and transparency regarding data use are some of the key challenges. AI could transform sleep medicine by enhancing diagnostic accuracy and personalizing treatment plans. Effective collaboration between clinicians and AI experts is necessary to use AI optimally in clinical settings.

This chapter provides a comprehensive introduction to the foundational concepts of Artificial Intelligence (AI), Machine Learning (ML), and Big Data, emphasizing their interdependence and transformative impact across modern industries. The chapter begins by defining AI as the capability of machines to perform cognitive functions traditionally associated with human intelligence, and clarifies the relationship between AI and ML, highlighting ML as a critical subset focused on data-driven learning and prediction. It explores the core methodologies of ML, including supervised, unsupervised, and reinforcement learning, and discusses the challenges posed by the exponential growth of digital data, particularly the need for scalable data collection and labeling techniques. The chapter further examines the concept of Big Data, characterized by its volume, velocity, and variety, and its essential role in enabling advanced analytics and real-time decision-making. The integration of AI, ML, and Big Data is presented as a catalyst for innovation in sectors such as manufacturing, healthcare, and especially insurance. In the insurance industry, these technologies are revolutionizing operations through automated claims processing, fraud detection, personalized services, and predictive risk management. The chapter also addresses the challenges of integrating AI and Big Data, including data quality, cybersecurity, and ethical considerations, and underscores the competitive imperative for organizations to adopt these technologies.

The improvement of observation capabilities has expanded the scale of new data available for time domain astronomy research, and the accumulation of observational data continues to accelerate. However, traditional data analysis methods are difficult to fully tap the potential scientific value of all data. Therefore, in the current and future research on light curve analysis, it is inevitable to use artificial intelligence (AI) technology to assist in data analysis in order to obtain as many candidates as possible with scientific research goals. This survey reviews important developments in light curve analysis over the past years, summarizes the basic concepts in machine learning and their applications in light curve analysis and concludes perspectives and challenges for light curve analysis in the near future. The full exploration of light curves of variable celestial objects relies heavily on new techniques derived from promotion of machine learning and deep learning in the astronomical big data era.This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence

Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future

Artificial Intelligence in Nephrology: Clinical Applications and Challenges

Today, artificial intelligence (AI) has significantly transformed all spheres of society. One of the tasks of the education system is to prepare children for successful lives in a modern technological world, ensuring their professional realization and demand in the labor market. In this study, we describe the conduct of a class with students within the framework of extracurricular activities focused on artificial intelligence: terminology is introduced, an approximate lesson plan is provided, and methodological recommendations are given. During the class, children become familiar with the concepts of artificial intelligence and machine learning, the differences between them, the concept of computer vision, the stages of machine learning, the steps of object identification, data acquisition and classification, examples of image recognition projects, decision trees, the concept of neural networks and their training, and they also teach a machine with built-in artificial intelligence to "see" objects. Throughout the lesson, various games were proposed to help understand and assimilate complex concepts. The uniqueness of these classes lies in their duration and the variety of activities. They can be conducted in summer camps, during scientific studies/research during holidays. Work on project implementation takes place in small groups, which directly contributes to the development of soft skills. It also provides an opportunity to organize competitions between groups, present one’s project to other groups, compare different solutions to a particular problem, and accordingly improve one’s own project. Special attention should be paid to the use of artificial intelligence in robotic systems. This combination allows for the expansion of the study of traditional STEM subjects. The article presents the experience of conducting classes within the volunteer project "Ukrainian Hacker School" for children. Using the example of a class involving Zumi, the main approaches and features of AI learning for children aged 10-16 are revealed.

Human's can't imagine machines independent. From Vacuum Tubes technology to transistors and now with Integrated chip (IC), semiconductor technology defined another industrial revolution. The modern industrial twirl is already in transpire. Artificial intelligence (AI) is ushering in the new age of technologies that are super fast, more flexible, much potential, and initiating the world more digitally connected. No doubt the potential of IoT and AI to clean up the environment is cosmic. A computational concept like fuzzy logic, neural network, and machine learning will change the perspective of machine how they are used now. Our study reveals retrospect progression in the internet of things and artificial intelligence technologies to curb ecological pollution. New generation AI-enabled intelligent control systems are optimized with a complex algorithm to identify and eliminate pollutants for environmental clean-ups. With AI automation pollution management and mitigation, developments are examined. Adopting tech-savvy IoT and AI technology, dependency on machines can be seemly employed. For example; Transportation Management System using AI and IOT can be used to determine the best shipping routes by cutting fuel and gas consumption. To some extent ICT, wireless technology adds a contribution to reducing E-Waste. ICT together with IoT acts as a mediator while renting and sharing devices among people with different industries. AI helps to solve complex data analysis that supports Quick Decision System and policymaking. With intelligent robots, we can monitor and control acidification on Soil and water. The concept of neural network and fuzzy logic are greatly used to develop intelligent machines that predict different gas rates and chemical processes. AI machines that continuously check the pH level of water, Analysis Air Quality Index can be built to monitor water resources and atmosphere. Some AI robots can identify pollutants and helps to separate them. Now we are successful in a bifurcation of paper with plastic, Iron with aluminum. Such a method contributes to the waste management system appropriately. Our study also inspects the challenges of implementing AI, How data mining and big data management feed system.
 Authentication/Authorization work with industry and governing authority. Inspection of a biased algorithm, poor network, and security. There is no new route to achieve our goal. Our goal is to achieve paramount measures with two concepts in mind, that are Humans assist machines and Machines assist humans to control and prevent pollution in these critical situations. Both humans and machines together find an instant solution with renting and sharing that helps to minimize ecosystem effluence

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Identifying artificial intelligence (AI) invention: A novel AI patent dataset

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Artificial intelligence (AI) is an influential technology, which has helped many branches of science and technology accomplish an unimaginable progress. The field of nanoparticles has had more than a decade-long tryst with AI, but it is yet to fully realize the benefits of AI to the extent other branches of technologies have. In recent times, there has been a renewed interest in applying machine learning and similar AI concepts to produce new nanoparticles, which has resulted in exponential growth of research in that direction and resulting data. This research paper critically analyzes that data to explore the prevalent trends/methods in applying machine learning (ML) and data mining techniques in the field of nanoparticles. Beginning with delineating the challenges faced in implementing ML in the nanoparticles segment, this paper goes on to explore the various learning algorithms relevant to the field of nanomaterials, their applications, suitability, merits, and demerits. In the latter section of the paper, a detailed analysis (in the form of a flow chart) spells out the considerations to be taken while collecting and constructing data, choosing and applying appropriate ML algorithms. This paper reviews various ML algorithms used in recent articles. In essence, this paper compendiously provides information and prediction of nanoparticles necessary to successfully customize ML for nanomaterials vertical, especially for nanoparticles of medical significance and applications.KeywordsMachine learningArtificial intelligenceNanoparticlesData mining