The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document-centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.

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.