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.