The publication under consideration provides a detailed scientometric analysis of two hundred scientific publications, which were obtained from the Scopus database, and the main focus was the intersection of Magnetic Resonance Imaging (MRI), brain tumor research, and artificial intelligence (AI), or at least data science. The analytical process sharply defined the intellectual structure and development of the field from 2021 to 2025. Among the important observations was the predomination of the research interests directed at the deep learning models for segmentation and classification, the growing importance of Explainable AI (XAI) to mediate between the model's performance and clinical acceptance, and the flourishing of radiomics for oncological prediction. The geographical distribution of the field indicates that it is a world-wide activity with the major input coming from China, India, America, Germany, and Pakistan. The authors and institutions whose works have had the greatest impact are revealed, and they are frequently found being associated with the topics of taking the client in combination with data coming from different modalities through the process of data fusion and the clinical translation of technologies. This literature review maps out these trends in order to counsel the researchers who are just starting and in the meantime to indicate the directions of the future research, such as the need for solid and consistent; federated learning and creating lightweight models that are suitable for clinical deployment.

This article provides an extensive scientometric assessment of the research on deteriorating inventory models, relying on a dataset of 2227 publications obtained from the Scopus database. The goal of the analysis is to draw a map of the intellectual terrain of this small but important area within supply chain management and operational research. By means of bibliometric indicators, the most influential publications, leading authors, core research institutions, and contributing countries are revealed in the analysis. The methodology is based on the statistical analysis of publication metrics, including citation counts, to determine impact and influence. Among the main findings are the identification of the seminal works that have built up the discipline, the networks of collaboration among researchers, and the shift of research topics towards sustainability, preservation technology, and supply chain integration.

The swift development of artificial intelligence (AI) has shifted to a new paradigm, i.e., AI agents—independent entities that can sense, think and do different things in ever-changing surroundings. This paper, through a scientometric analysis, looks at the growing territory of AI agents’ research by sifting through data related to a wide-ranging dataset of academic articles. It maps out the key research directions, the most popular areas for application, the prominent methodological approaches, and the nascent difficulties. The results reveal that there has been a considerable increase in the research related to AI agents, which is mainly due to the progress made in the field of large language models (LLMs), multimodal AI, and agentic frameworks. The most important application fields are healthcare, education, manufacturing, finance, and smart cities. The research also points to the limitations in terms of ethics, security, and operations that would need to be worked through if AI agents are to be deployed in a responsible manner. This study not only presents an organized picture of the current situation, but also indicates new areas for researchers to explore.

Supply chain disruption has become a serious world's problem during the past few years. Many businesses lose their customers due to late delivery or shortage of raw materials. Thus, it is necessary to look for expert systems to handle such issues. The paper presents a scientometrics survey on the role of artificial intelligence on supply chain management. The study uses the Scopus database to collect data from 1995 to 2022. The study collects nearly 750 articles which are sorted based on their citation records. Using some scientometric tools, the study has determined that the decision support system has been the most important tool to handle disruption in supply chain management. Moreover, the study shows that most studies were accomplished in North America, and some were partnerships with China. The study also detected nine groups of researchers who contributed the most in the supply chain. Moreover, the study discusses the concept of uncertainties associated with mathematical modeling associated with supply chain management and categorizes different works according to the methods used to handle the uncertainties. Finally, the study explains some of the recent developments of the implementation of artificial intelligence in various areas of supply chain management such as waste management, blood supply chain, etc. The results indicate that artificial neural networks are the most popular technique used among researchers to provide more efficient solutions for supply chain management.

The interconnected problems of climate change, water security, and the transition to green energy are considered to be an important area for research. This scientometric analysis investigates the intellectual landscape and the emerging trends in research connecting "Green Energy" with "Water" and climatic events like "Drought," "Flood," and "Climate Change." Based on a dataset of 228 publications taken from Scopus, the study assesses the trends of publication, the main themes of research, the most influential authors, and the top journals. The results point to a field that is developing at a very fast rate, mostly relying on the studies of hydrogen production, integration of solar and hydropower, and sustainable management of the water-energy-food nexus. The main research areas include 1) technological innovations in hydrogen production by water splitting, 2) the effects and optimization of hydropower, 3) the use of solar energy in farming and water management, and 4) the economic and environmental aspects of the green energy transition. The leading contributors are China, the United States, and a number of European countries. This analysis presents a systematic overview of the structure of the field, and at the same time, it indicates the change from basic research in energy towards approaches that are integrated and systems-based in dealing with climate resilience and sustainable resource management.