Heart disease is one of the most common causes for death among human nations for many years. There have been substantial efforts to reduce heart diseases in the world. It is essential to implement the recent advances of data science to discover any symptoms of cardiovascular disease (CVD). Machine learning (ML) has given scientists a tool to detect early causes of such disease and this survey uses the combination of ML and CVD as a search keyword to determine 200 highly cited articles from the Scopus database. The study performs a survey on the data which were published from 2018 to 2025 and present possible road-map for future studies. The results indicate that a significant number of highly cited articles are published in Open Access journals such as PlosOne, IEEE Access and Scientific Report. In addition, the study presents seven different areas of research which have been under significant progress.

Acute Myeloid Leukemia (AML) remains a significant hematological malignancy where early and accurate diagnosis is critical for improving patient outcomes and therapeutic stratification. With the emerging understanding of liquid biopsies, research on extracellular vesicles (EVs), such as exosomes, and their microRNA (miRNA) cargo has evolved as a promising domain for AML diagnostics. However, a systematic review is imperative not only to consolidate existing knowledge but also to chart relevant and timely pathways for future research in the AML diagnostics domain. Against this backdrop, in this study, we conduct a bibliometric analysis of research at the intersection of Acute Myeloid Leukemia and exosome-based biomarkers. Our aim is to map the intellectual structure of the field, identify thematic clusters and influential works, and surface emerging and underexplored directions. The Web of Science database was queried on October 01, 2025, using a comprehensive Boolean search string against three thematic pillars: (1) Disease Focus, with the terms “Acute Myeloid Leukemia*”, “AML”, “APL”, "AML-M1", "AML-M6", and related synonyms; (2) Vesicle/RNA Technology, including “Exosome*”, “extracellular vesicle*”, "microvesicle*", and “miRNA” ; and (3) Biomarker Application, using keywords such as “Biomarker*”, “Early Diagnosis”, and “Liquid Biopsies”. We retrieved an initial corpus of 714 documents. Upon applying a two-stage curation protocol based on predefined inclusion criteria (such as language restrictions), a final analytical sample of 710 documents was established for the robust bibliometric analysis. From our analysis, we confirm that the burgeoning domains of AML diagnostics and exosome-based biomarkers are rapidly expanding, with specific molecules like miRNAs and exosomal proteins emerging as central enablers, research focusing on prognostic stratification and residual disease monitoring (MRD) integration, and clear gaps remaining in methodological standardization (e.g., EV isolation) and clinical validation, highlighting promising directions for future investigation.

In contemporary medicine, two trends are of particular significance: the establishment of Telehealth as a fundamental component of healthcare delivery, and the emergence of Large Language Models (LLMs) as a transformative category of artificial intelligence. The convergence of these domains has created a new, dynamic, and rapidly expanding research frontier. Despite this escalating interest, a comprehensive map of the field's intellectual foundations, key contributors, and thematic progression has yet to be established. This study addresses this critical knowledge gap by employing a quantitative scientometric methodology to systematically map the intellectual structure and evolutionary trajectory of research at the intersection of Telehealth and LLMs. We conducted a comprehensive bibliometric analysis of 670 scientific documents extracted from the Web of Science (WoS) Core Collection database, spanning the period from 1997 to Oct 2025. The analysis utilized performance metrics and network mapping tools to identify publication dynamics, influential actors, and core conceptual themes. The findings reveal a field in a state of “Hypergrowth“, characterized by an exponential increase in scientific production, particularly after 2021. The United States and China are identified as the dominant leaders in research output. Thematic analysis demonstrates a clear paradigm shift within the literature: an evolution from a broad focus on general artificial intelligence and machine learning applications toward a more specialized and intense concentration on the capabilities and implications of LLMs and Generative AI. This research provides the first large-scale quantitative map of the Telehealth and LLM landscape. It documents a field that is maturing at an accelerated rate, creating an urgent need for scholarly and practical frameworks that bridge the gap between rapid technological innovation and the slower-moving, yet critical, domains of clinical validation, regulatory oversight, and ethical considerations. The insights provided herein offer a data-driven foundation for researchers, policymakers, and practitioners to navigate and contribute to this critical and rapidly evolving field.

The COVID-19 pandemic has created an exponential trend in global research mobilization, with vaccines as a primary objective. This present survey gives a scientometric study on the landscape of research concerning the COVID-19 pandemic and vaccines, using a dataset of 19,749 records from the Scopus database where only 200 records are chosen for the review. The analysis maps the conceptual structure and dynamics of this area by looking at the publication trends, key contributors, core research themes, and impact of the published papers. The results have disclosed an exponential trend in publications, peaking in 2021-2022, driven by the quick requirement for development, evaluating, and deploying vaccines. The study was investigated by large-scale international collaborations, with prolific contributions from universities as well as vaccine makers in the United States, China, and Europe. High-impact journals like The Lancet and The New England Journal of Medicine considered critical dissemination channels. Thematic clusters are dominated by vaccine development and immunology, real-world effectiveness, SARS-CoV-2 variants and immune evasion, vaccine safety, and public acceptance. The evolution of study is concentrated on from initial clinical trials to real-world evidence, variant-specific challenges, and eventually, long-term impact and systemic lessons. The survey gives a comprehensive review of a defining scientific effort, showing the collaborative and rapid-response nature of research during a global health crisis.

Bidirectional Encoder Representations from Transformers (BERT) has become a paradigm shift in natural language processing (NLP). This scientometric study analyzes a curated dataset of 200 highly-cited Scopus publications to map the intellectual landscape and research trajectories catalyzed by BERT and other transformer models. The study discloses a quick evolution from foundational architectural innovations and pre-training paradigms to widespread domain adaptation, rigorous model optimization for efficiency, and critical examination of model capabilities and societal effects. The literature shows BERT's role as a foundational model, successfully implemented in diverse fields such as biomedicine, education, and software engineering, while simultaneously spurring substantial research into compression, quantization, and efficient inference. A parallel and influential strand of studies emerged concentrated on “BERTology”, giving the linguistic knowledge and biases encoded within these models, and addressing ethical concerns regarding their deployment. The study synthesizes these developments, presenting how BERT not only set new performance benchmarks but also built a new paradigm for transfer learning and spurred a self-critical research community, ultimately paving the way for the present era of large language models.

At the edge of the network, the exponential increase of data produced along with the growing concerns over data privacy coming from regulations and society have all together triggered the rise of Federated Learning (FL) as the main approach in distributed machine learning (DML). Fed learning allows the model training to be performed on decentralized devices or data silos even without the raw data being transferred. Hence, FL is completely in line with the objectives of the privacy-preserving techniques. In this paper, we carry out a scientometric analysis on the 200 most cited papers, which are the first 200 papers at the intersection of "Distributed Machine Learning," "Federated Learning," and "Privacy-Preserving" published between 2020 and 2024, and the Scopus database is where they are indexed. The literature of publication trends, prominent authors and works, the thematic clusters, and research fronts that are changing are all systematically examined in this study; hence, the intellectual landscape of this fast developing field is mapped out. Our findings point to the existence of certain streams of research such as the algorithms with differential privacy being the mainstay, secure aggregation methods through the use of homomorphic encryption and multi-party computation, blockchain-based FL systems which ensure security and trust, and resource-efficient FL that supports IoT and edge computing. The results also show an area that is nearly enjoying a complete transformation as a result of the overpowering need to address the triad of model quality, data protection, and system efficiency. The review not only encourages researchers, and practitioners but also helps the policymakers by providing the current trend to which the key challenges can be identified and the future directions in privacy-preserving distributed intelligence anticipated.

The emergence of large language models (LLMs), like ChatGPT, has caused a major shift in all academic and professional fields; education being the one impacted the most deeply. The first 200 most cited articles from Scopus on the topic of "large language models" and "education" were selected for a scientometric analysis of this survey. The aim is to map the intellectual landscape, specifying dominant research themes, key contributors, methodological trends, and the emergent challenges that researchers faced during this initial, explosive phase of research. This review systematically analyzes the publication trends, authorship patterns, geographical and institutional contributions, and the conceptual structure of the literature to provide a quantitative and qualitative snapshot of a field that is rapidly changing. The analysis uncovers a domain where exploratory studies, conceptual debates, and early empirical validations dominate, and where the focus of research is mainly on higher education and medical training. The major issues being discussed are academic integrity, assessment redesign, and the ethical integration of AI, while promising applications are personalized learning, automated feedback, and content generation. This survey is a reference for a better understanding of the roots and forefronts of LLM-related educational research as it passes from the initial reaction to systematic integration.

The introduction of Large Language Models (LLMs) in the healthcare sector is a major step, which can for sure transform all aspects of medicine including practice, research, and education. This review of publications presents the first 200 most cited articles which were obtained from a Scopus search on the terms "large language models" and "healthcare". The purpose was to elucidate the knowledge and trends in this fast-growing area. The analysis looks at the yearly publications, the main journals, the most prominent authors and institutions, the leading research areas, the methodological approaches used, and the ethical and regulatory issues that are the most talked about. The results show an increase in the number of scholars interested in this field, especially in 2023 and 2024. Besides that, it was found that there is a lot of high-impact publications going on in the leading multidisciplinary and specialized medical journals areas. The largest research areas that were found are: clinical trials and their outcomes, ethical and governance frameworks, educational integration, and technological advancements and surveys. The research is mainly focused on testing the performance of LLMs like ChatGPT in particular medical tasks but in the background, there are profound concerns about their accuracy, bias, and safety. This review presents the current knowledge state, points out the most active and leading research fronts, and recognizes the gaps with future directions hence it offers the most basic reference for researchers, doctors, and policymakers who will be dealing with the LLMs incorporation into the healthcare system.

The deployment of Large Language Models (LLMs) in the financial field is one of the most forward-looking and quickly changing AI research areas. This paper presents a thorough scientometric study of the top 200 most cited articles, which deal with the conjunction of LLMs, finance, and stock markets, and are indexed in Scopus. The analysis carries out a detailed examination of the publication patterns, influential authors and institutions, main research areas, methods used, and the intellectual framework of this new field. The disclose of the research output has been increased enormously; the main form of it is conference papers, and it is all through collaboration between the global academic and industrial research centers. Financial sentiment analysis and market prediction, the building and evaluating of domain-specific financial LLMs (FinLLMs), and lastly the application of the mentioned FinLLMs in areas such as financial analytics and decision-making support are examples of research that has been clustered together. Nevertheless, a big part of the research is directed towards trust, ethics, and risks in the financial domain. Moreover, the study points out that practical applications, such as algorithmic trading, risk management, and compliance with regulations, are among the most highlighted in the fields of LLMs in finance. This scientometric review offers a primary map of the vast conceptual territory marked by high-impact research, which means it can be very helpful to researchers, practitioners, and policymakers in getting a better understanding of the existing situation and thus, pointing out future research directions in the area of LLMs in finance.

The deployment of Large Language Models (LLMs) in the financial field is one of the most forward-looking and quickly changing AI research areas. This paper presents a thorough scientometric study of the top 200 most cited articles, which deal with the conjunction of LLMs, finance, and stock markets, and are indexed in Scopus. The analysis carries out a detailed examination of the publication patterns, influential authors and institutions, main research areas, methods used, and the intellectual framework of this new field. The disclose of the research output has been increased enormously; the main form of it is conference papers, and it is all through collaboration between the global academic and industrial research centers. Financial sentiment analysis and market prediction, the building and evaluating of domain-specific financial LLMs (FinLLMs), and lastly the application of the mentioned FinLLMs in areas such as financial analytics and decision-making support are examples of research that has been clustered together. Nevertheless, a big part of the research is directed towards trust, ethics, and risks in the financial domain. Moreover, the study points out that practical applications, such as algorithmic trading, risk management, and compliance with regulations, are among the most highlighted in the fields of LLMs in finance. This scientometric review offers a primary map of the vast conceptual territory marked by high-impact research, which means it can be very helpful to researchers, practitioners, and policymakers in getting a better understanding of the existing situation and thus, pointing out future research directions in the area of LLMs in finance.