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Introduction
Monte Carlo (MC) methods are widely considered the gold standard for calculating radiation dose distributions in the medical physics field owing to their recognized ability to simulate the stochastic nature of particle transport and interactions in matter with high accuracy [1-3]. MC methods are being adopted increasingly in various medical physics applications for ionizing radiation, including diagnostic imaging, radiotherapy, nuclear medicine, and radiation shielding [4-8]. In addition, they can be employed to accurately model radiation transport and estimate dose distributions within complex patient anatomies, including air cavities, surface irregularities, and heterogeneous tissue interfaces [1,9,10]. This wide applicability highlights the increasing importance of MC methods in clinical and research contexts of medical physics, driven by the ongoing and increasing demand for complex and precise dose calculations [11].
With the global expansion of MC-based research in medical physics, Korea has also experienced notable growth in this field, which has been driven by the rising incidence of cancer and increasing demand for treatment based on radiation [12]. This growth has been supported by various national research initiatives and the continued development of advanced clinical infrastructure. Furthermore, Korean research groups have applied MC methods across various subfields of medical physics, including diagnostic imaging, radiation shielding, and dosimetry, thereby contributing to technological advancements and clinical practice [13-17]. Given the rapid expansion of MC-based research, a timely and comprehensive review of research trends can provide meaningful insights into the dominant themes, key contributors, and emerging trends.
Thus, in this study, we performed a bibliometric analysis of MC-related research activities in the Korean medical physics community from 2015–2024 with a focus on Korean-led publications with at least one corresponding author affiliated with a Korean institution. Bibliometric analysis is a quantitative research method that statistically evaluates academic publications by examining authorship, institutional affiliations, keyword usage, and citation metrics [18,19]. Such analyses can be visualized effectively using graphs and tables, thereby providing insights into various aspects, such as development trajectories, emerging research focus areas, and intellectual structures of the field [20]. In addition, bibliometric techniques can be used to reveal research gaps and bottlenecks, which supports strategic planning for future investigations. This analysis was performed to provide a comprehensive and scientifically grounded overview of the evolution and current state of MC-based research trends and intellectual structures in Korean medical physics.
Materials and Methods
In this study, we adopted the standard science mapping workflow proposed by Zupic and Čater [21], which comprises five sequential steps, i.e., study design, data collection, data analysis, data visualization, and interpretation.
1. Study design
This study was designed to systematically investigate research activities involving MC methods in the field of medical physics, with a strict focus on publications led by Korean research groups from 2015 through 2024 (inclusive). The objectives were to quantify publication trends, identify the dominant research themes and thematic structures, and characterize the collaboration patterns among authors, institutions, and countries.
2. Data collection
Primary records were retrieved from the Scopus database (snapshot: September 30, 2025), and the following Boolean search query was utilized to retrieve publications on MC-based medical physics:
TITLE-ABS-KEY("Monte Carlo") AND TITLE-ABS-KEY("medic*" OR "radi*" OR "therapy" OR "treatment" OR "imag*" OR "dosimet*" OR "diagnosis" OR "detector" OR "accelerator" OR "in-vivo" OR "beam" OR "scanning")
Note that the search was limited to publications categorized as “Articles” and “Reviews” from January 2015 to December 2024 (inclusive). Here, only publications with a corresponding author affiliated with a Korean institution were included to ensure that we retrieved Korean-led work. Initially, records with at least one author affiliated with a Korean institution were retained (n=1,013) because Scopus does not support filtering by the corresponding author’s affiliation at the search stage. Then, the retrieved documents’ titles and abstracts were reviewed to assess their relevance to MC-based research in medical physics and exclude irrelevant documents. Then, the corresponding author status and affiliations were identified from the Scopus “Correspondence Address” field and verified against publisher metadata. In this process, only records with one or more corresponding author(s) affiliated with a Korean institution were retained, yielding a total of 307 publications.
To complement the limited coverage of Scopus, a supplementary search was performed using Google Scholar because Google Scholar applies less stringent inclusion criteria than Scopus [19]. This search focused on additional publications by the corresponding authors identified in the Scopus-derived dataset and potentially relevant studies published in journals not indexed in Scopus. Duplicate records across databases were identified and removed based on digital object identifiers. Here, three additional publications were identified, resulting in a final dataset with a total of 310 documents. Fig. 1 illustrates the overall data collection workflow.
3. Data analysis
Quantitative bibliometric analyses were performed using Microsoft Excel. The annual publication outputs, journal classifications, journal impact factor (JIF), and citation metrics were compiled and evaluated. Note that the journal classifications were defined using three mutually exclusive categories, i.e., (1) Web of Science Science Citation Index Expanded (SCIE)-indexed journals, (2) Korea Citation Index (KCI)-indexed journals not included in SCIE, and (3) Others (not indexed in SCIE or KCI). The subject areas followed the Scopus ASJC categories implemented in SciVal. Here, multilabel full-counting process was applied; thus, the totals may exceed 100%. The citation metrics were obtained from the Scopus database on September 30, 2025. To mitigate citation lag, average JIFs were computed by excluding publications from the most recent 3 years (2022–2024). In addition, self-citation–included and self-citation–excluded values were considered to provide a comprehensive citation impact assessment. Furthermore, the annual journal impact was computed by assigning the JIF reported in Clarivate’s Journal Citation Reports (JCR) for that article’s publication year to each article, and then aggregating by year. For each year, the accumulated JIF value was taken as the sum of publication-year JIFs across all articles, and the mean JIF was that sum divided by the number of articles. Note that non-JCR journals were excluded from the JIF-based calculations.
The nonparametric Mann–Kendall test, which assesses whether a statistically significant monotonic upward or downward trend exists in a time series without requiring the data to conform to a specific distribution, was applied to evaluate the temporal trends in the publications outputs and JIF. Here, the test statistic (S), Kendall’s tau (τ), standardized normal score (z), and significance level (P) were computed to determine the direction, strength, and significance of the trend.
The topical analyses identified the dominant research areas and thematic developments using keyword frequency analysis and co-occurrence network mapping. Here, the titles and abstracts were case-folded, Unicode-normalized, tokenized, and lemmatized, and punctuation, numerals, and stopwords (general and domain-specific) were removed. In addition, multiword expressions denoting single concepts (e.g., “Monte Carlo,” “proton therapy,” and “single photon emission computed tomography”) were merged into compound tokens, and domain synonyms were harmonized (e.g., “MC”→“Monte Carlo” and “BNCT”→“boron neutron capture therapy”). Furthermore, the MC toolkit frequencies were measured as publication counts (paper-level presence) rather than term-occurrence counts. A dictionary-based, case-insensitive matcher scanned the titles, abstracts, and author keywords for canonical tools (GATE, Geant4, TOol for PArticle Simulation [TOPAS], Monte Carlo N-Particle [MCNP], EGSnrc, FLUktuierende KAskade [FLUKA], Particle and Heavy Ion Transport code System [PHITS], and Penetration and ENErgy LOss of Positrons and Electrons [PENELOPE]). Whole-word matching and context filters were used to exclude homographs.
Building on this domain-specific keyword analysis, we investigated emerging research themes, including applications of artificial intelligence (AI). In this process, a lexicon of AI-related terms based on the current trends in medical physics and AI research was established. This lexicon included terms such as “artificial intelligence,” “deep learning,” “machine learning,” “neural network,” “transformer,” “reinforcement,” and “generative adversarial network.” All publications were screened for the presence of these keywords in the titles, abstracts, and author-provided keywords using a keyword-based search strategy.
Institutional output was tallied using a full-counting scheme based on all distinct author affiliations listed in each paper. Note that each paper contributed one count to every unique institution. In addition, institutional affiliations were normalized using Scopus affiliation IDs and manual curation, and multicampus units were aggregated to the parent university. Country-level counts followed the same rule. Collaboration types were categorized based on the complete set of author affiliations. Here, publications in which all authors were affiliated with a single institution were classified as institutional collaborations, and publications including two or more distinct Korean institutions but no foreign affiliations were defined as national collaborations. Publications involving at least one non-Korean affiliation were classified as international collaborations, and those authored by a single researcher were categorized as single-author papers. Then, institution–institution and country–country co-authorship networks were constructed, where the node sizes represented the number of coauthored papers, and self-links were excluded from the network visualization.
4. Data visualization and interpretation
Quantitative trends, e.g., annual publication output, citation metrics, and JIFs, were visualized using Python, which was employed to generate frequency charts illustrating the usage frequency of various MC toolkits and the distribution of collaboration types. The network-based analyses, including co-authorship and keyword co-occurrence, were visualized using VOSviewer (v1.6.20), which is a widely employed tool for bibliometric mapping [22]. Prior to conducting the keyword analysis, text preprocessing was performed to ensure consistent term representations. Multiword expressions that frequently appeared together and represented a single concept (e.g., “Monte Carlo”) were merged into unified tokens to prevent artificial fragmentation during analysis. This normalization enabled compound terms to be counted and analyzed as cohesive concepts. In VOSviewer, the visualization parameters were adjusted to enhance the interpretability of the network maps. Here, the visualization scale was set to 1.0, and the weights attribute for node sizing and link thickness was defined by the number of occurrences. In addition, the size variation of the labels and connecting lines was set to 0.5 to balance visibility between high-frequency and low-frequency items, and the maximum label length was limited to 30 characters to avoid overlap and ensure sufficient readability. Normalization of the co-occurrence strength was performed using the association strength method, which minimizes bias due to high-frequency terms. The clusters were color-coded according to the default modularity-based community detection of VOSviewer, representing distinct thematic domains in the analyzed dataset.
To highlight the most frequently used terms in the publications, a stylized word cloud was generated using Wordclouds (https://www.wordclouds.com/). Over the past decade, such visualizations have facilitated effective interpretation of publication performance, evolving research themes, and collaboration patterns in MC-based medical physics research at Korean institutions.
Results
1. Quantitative analysis of publication trends
In this study, a total of 310 publications on MC-based medical physics research published by Korean groups between 2015–2024 were identified. Fig. 2 shows the annual distribution of these publications categorized by journal type, i.e., SCIE, KCI, and other international and domestic journals. The annual publication output ranged from 25 to 40 papers, with the highest and lowest numbers observed in 2021 (n=40) and 2018 (n=25), respectively. Among the analyzed publications, 88.9% were published in SCIE-indexed journals, and 8.4% appeared in KCI-indexed journals that were not included in the SCIE. The remaining 2.9% were published in journals indexed in neither SCIE nor KCI; thus, they were categorized as “Others.” SCIE-indexed publications dominated the overall output throughout the study period, consistently comprising more than two-thirds of the yearly publications. Furthermore, the KCI-indexed publications exhibited year-to-year variability, whereas those in the “Others” category remained marginal (never exceeding two papers in any single year throughout the study period).
Table 1 summarizes the top 10 journals that published MC-based medical physics research by Korean groups in the period 2015–2024. The journal distribution revealed a strong publication bias toward physics-oriented and medical physics–specific journals. Domestic physics journals, particularly the Journal of the Korean Physical Society and New Physics: Sae Mulli, accounted for a substantial proportion of the output. International physics and engineering journals, e.g., Nuclear Engineering and Technology, Journal of Instrumentation, and Nuclear Instruments and Methods in Physics Research, Section A, also featured prominently.
We found that high-impact and medical physics–specific journals, including Medical Physics, Physics in Medicine and Biology, and Physica Medica, were well represented, demonstrating the active application of MC methodologies in clinically relevant research. Many of these journals are classified into the first or second quartiles (Q1 or Q2) in the JCR, which indicates that a significant portion of MC research in Korea is disseminated through internationally competitive journals.
In addition, including multidisciplinary journals, e.g., PLoS ONE, reflects outreach beyond medical physics, possibly targeting broader audiences for interdisciplinary studies involving biology, imaging, or computational science.
Fig. 3 shows the distribution of publications by subject area. Note that a number of publications were assigned to multiple Scopus subject areas; thus, the total counts exceed 100%, reflecting a multilabel counting approach that ensures all relevant subject areas are represented. The publications were predominantly classified in physics and astronomy (52.7%), which highlights the strong methodological foundation of MC-based medical physics research in Korea and its alignment with physics-focused journals. The second largest category, i.e., medicine (39.1%), highlights the substantial clinical impact of MC approaches, particularly in diagnostic imaging, radiotherapy, and nuclear medicine applications. The biochemistry, genetics, and molecular biology (20.8%) category reflect interdisciplinary studies involving molecular imaging, radiopharmaceuticals, and the biological modeling of radiation effects. Other categories, e.g., energy (11.1%) and health professions (10.4%), indicate the broader relevance of MC simulations beyond physics and medicine, including radiation protection, energy applications, and professional healthcare applications.
The annual citation trends of MC-based medical physics publications authored by Korean research groups from 2015–2024 are shown in Fig. 4. The total number of citations reached a maximum in 2019, with 338 citations (excluding self-citations), followed by a decline toward 2024, reaching 36 citations. When self-citations were included, the overall pattern remained similar, with higher values observed (425 citations in 2019). Across the entire study period, the overall self-citation rate was 27.2%, with annual rates ranging from 39.2% in 2016 to 20.3% in 2022. Focusing on the period 2015–2021 and excluding self-citations, the average total citations per publication were 189.3, corresponding to a mean value of 5.9 citations per paper. The results of the Mann–Kendall trend analyses revealed no statistically significant monotonic trend in the total citations over time (S=1, τ=0.048, z=0.000, P=1.000), whereas citations per publication exhibited a significant decreasing trend (S=−27, τ=−0.600, z=−2.326, P=0.020). These results indicate that, while the overall citation volume fluctuated, the average citation impact per publication declined significantly during the early phase of the study period.
Fig. 5 shows the annual journal impact trends for MC-based medical physics publications in Korea from 2015–2024, which were evaluated using the total and average JIFs. Generally, the total annual JIF showed an upward trajectory over this period, suggesting that Korean research groups increasingly published in journals with greater citation influence. Here, the average JIF per publication was 2.3, which indicates that MC-based medical physics research was published in journals with a JIF value of approximately 2.3. In addition, the results of the Mann–Kendall trend analyses confirmed statistically significant monotonic increases in both indicators: total annual JIF (S=31, τ=0.689, z=2.683, P=0.007) and average JIF per publication (S=37, τ=0.822, z=3.220, P=0.001). These results demonstrate a strong and consistent positive correlation with publication year, reflecting a continuous improvement in terms of the journal quality and the visibility of Korean MC-based research.
2. Thematic analysis of research topics
The thematic landscape of the MC-based medical physics research conducted by Korean groups from 2015 through 2024 is illustrated in Fig. 6. The figure shows the 100 most frequently used terms visualized in a stylized word cloud, highlighting the relational structure of frequently used terms and their relative prominence in the literature.
The most frequently used term was “Monte Carlo” (252 occurrences), followed by “Dose” (195), “Beam” (158), and “Phantom” (136), which were commonly associated with studies focused on radiation dose calculations using anthropomorphic models. Additional commonly co-occurring terms included “Image” (110), “Therapy” (105), “Radiation” (98), “Energy” (97), “Treatment” (97), and “Geant4” (84). Among the 310 publications analyzed in this study, only seven (2.3%) were identified as AI-related using the predefined keyword lexicon.
Fig. 7 shows the analysis of the author-provided keywords in the same dataset, revealing patterns that are consistent with the co-occurrence word analysis. Here, “Monte Carlo” remained the most frequently used keyword (n=119), followed by “Proton therapy” (n=25) and “Geant4” (n=21); other commonly used keywords included “Dosimetry” (n=8), “Radiotherapy” (n=8), “Single Photon Emission Computed Tomography (SPECT)” (n=7), “Gamma camera” (n=7), “Boron Neutron Capture Therapy (BNCT) ” (n=7), and “Prompt gamma” (n=7).
To evaluate the usage patterns of MC simulation toolkits in Korean MC-based medical physics research, a detailed review of publications was performed to identify studies that explicitly reported the use of specific MC codes. Here, Geant4 appeared to be the only specific MC simulation toolkit in the top keyword co-occurrence analysis; however, a more detailed review identified 281 studies that explicitly reported the use of specific MC codes. The most widely used tool was the Geant4 Application for Tomographic Emission (GATE), which appeared in 79 publications (28.1%), followed by Geant4 (n=42, 14.9%) and the TOPAS (n=33, 11.7%). Note that all three tools are based on the Geant4 toolkit and accounted for 154 publications (54.7%).
Other MC codes included MCNP with 37 publications (13.2%), the Electron Gamma Shower code of the National Research Council of Canada (EGSnrc) (n=26, 9.3%), FLUKA (n=24, 8.5%), the PHITS (n=21, 7.5%), and PENELOPE (n=19, 6.8%).
3. Institutional and collaboration network analysis
The top five institutions accounted for most MC-based medical physics publications in Korea from 2015–2024, collectively producing 202 out of the 310 publications (65.2%). Yonsei University was the leading contributor (n=68, 21.9%), followed by Seoul National University (n=37, 11.9%) and Hanyang University (n=35, 11.3%). Other major contributing institutions included the Korea Institute of Radiological and Medical Sciences (n=32, 10.3%) and the Catholic University of Korea (n=30, 9.7%).
Regarding the distribution of collaboration types, over half (158 publications, 51.0%) of the 310 publications analyzed in this study involved only national collaborations, in which multiple authors were affiliated with different domestic institutions. In contrast, institutional collaboration, defined as co-authorship within a single institution, accounted for 82 publications (26.5%), and international collaborations were observed in 65 publications (21.0%). Note that only five publications (1.6%) were authored by a single researcher.
Fig. 8 shows the international research collaboration network for MC-based medical physics publications involving Korean research groups from 2015–2024. Among the collaborating countries, the United States had the highest number of joint publications (n=42). Other countries with fewer collaborations included Japan (n=6), France (n=5), Singapore (n=3), China (n=2), Vietnam (n=2), Nepal (n=2), and the United Kingdom (n=2).
Discussion
The consistent publication of Korean MC-based medical physics research in SCIE-indexed journals demonstrates the sustained adoption of MC methods and an active commitment to disseminating research output within the international academic community.
The distribution of publications by subject area highlights the interdisciplinary and clinically oriented nature of MC-based medical physics research in Korea. Most studies focused on physics and medicine, reflecting various applications, e.g., dose calculations, treatment planning, and therapeutic imaging. In addition, growing contributions from biochemistry, genetics and molecular biology, engineering, and computer science indicate an expanding interdisciplinary landscape beyond conventional domains.
The results of the citation analysis revealed temporal fluctuations in citation counts, which likely reflect the combined effects of publication age, self-citation behavior, and shifts in research activity. Self-citations accounted for approximately 25% of the total; however, their inclusion did not alter the overall declining trend, indicating that the observed reduction was not primarily driven by self-citation bias. The reduction in mean citations per publication after 2020 can be explained by cohort maturity effects because recently published papers naturally have shorter citation windows. Note that a few highly cited papers may act as outliers and inflate the annual averages in certain years. However, the general pattern suggests a stabilization or plateau in citation growth rather than a decline in research impact. Furthermore, year-to-year variations in citation metrics may also arise from compositional differences, e.g., changes in journal selection, collaboration patterns, or topical focus, rather than reduced research quality or visibility.
The increasing average JIF per publication since 2018 suggests a gradual shift toward journals with higher impact, which implies that researchers have strategically aimed to enhance scientific influence and international reach. The overall upward trend in the accumulated JIF reflects an increase in publication volume and diversification into journals with greater citation potential. Nevertheless, the annual fluctuations in JIF may partly arise from compositional factors, e.g., the varying mix of journal categories and research themes, rather than abrupt changes in the quality of research. This pattern indicates that while Korean groups have steadily improved the scientific recognition of MC-related studies, consistent efforts to publish in high-impact journals remain essential to continue strengthening the global competitiveness of this research field.
The distribution of frequently used keywords reflects the methodological foundation and scope of MC research applications in medical physics. For example, the dominance of the term “Monte Carlo” and related toolkits, e.g., “Geant4,” confirms the centrality of MC simulation as a core computational method across diverse studies. The terms “phantom” and “dose” followed closely, which indicates a strong focus on radiation dose assessment using phantoms. Recent international bibliometric studies have reported rapid advancements in AI-driven radiotherapy research, especially in autocontouring, dose prediction, and adaptive treatment planning using deep learning techniques [23]. In contrast, AI methodologies have been adopted relatively slowly in the Korean MC-based medical physics community, which suggests a more conservative research focus.
Furthermore, among the author-provided keywords, the prominence of several terms, including “radiotherapy,” “proton therapy,” and “BNCT,” highlights the clinical relevance of MC methods, particularly in the context of advanced radiation treatment modalities. In parallel, the appearance of other terms, e.g., “imaging,” “SPECT,” and “gamma camera,” reflects the integration of MC simulations into medical imaging systems, supporting accurate system modeling and quantitative image analysis.
The prevalent use of MC toolkits, e.g., Geant4 and MCNP, along with the increasing adoption of PHITS and FLUKA, reflects methodological diversity and active engagement with simulation tools in medical physics research. In addition, regular annual training courses on Geant4 and MCNP further support user proficiency and expand their applications across the field. Among these toolkits, Geant4-based platforms, including the general-purpose Geant4 toolkit and application-specific platforms, e.g., TOPAS and GATE, dominate the current research output. The Geant4 toolkit provides a flexible and extensible framework that can be employed to model a wide range of particle transport scenarios across energy domains. In contrast, GATE and TOPAS are tailored for specific applications, e.g., tomographic emission simulations and medical applications of ionizing radiation, respectively. Both offer preconfigured physics models, dedicated geometry modules, and streamlined workflows, thereby enabling the efficient implementation of complex simulations in medical physics and allowing researchers to focus on application-specific objectives without requiring extensive customization of the underlying Geant4 codebase. The increasing use of such MC codes indicates the growing preference for simulation environments that can handle complex geometries, diverse particle interactions, and application-specific requirements for radiotherapy, nuclear medicine, and diagnostic imaging.
The analysis of author affiliations and collaborative networks demonstrated that a significant portion of MC-based medical physics research in Korea has been driven by several leading institutions with well-established infrastructure. MC simulations require substantial computational resources and dedicated infrastructure, which these institutions have already developed, thereby enabling sustained and large-scale research efforts. In addition, many of these institutions maintain strong collaborative relationships with affiliated hospitals, facilitating the seamless integration of simulation studies with clinical data for validation. Notable examples include Yonsei University (Severance Hospital), Seoul National University (Seoul National University Hospital), Hanyang University (Hanyang University Hospital), and the Catholic University of Korea (Seoul St. Mary’s Hospital). The Korea Institute of Radiological and Medical Sciences also collaborates closely with universities, e.g., the University of Science and Technology, combining research infrastructure with access to relevant clinical datasets.
With their well-established infrastructure and strong hospital affiliations, these leading institutions have fostered strong domestic collaborations; however, the relatively limited level of international collaboration indicates a key area for the potential improvement of the current research landscape. Although MC-based medical physics research in Korea has been advancing steadily through effective national partnerships, the low rate of international collaboration may restrict access to a wide range of international expertise, cutting-edge technologies, and shared research infrastructure. This suggests that the potential for broader global participation has yet to be fully realized, and although international collaborative efforts, e.g., with the United States and Japan, are evident, these connections remain relatively modest compared with global trends in scientific cooperation.
Collectively, the findings of this study provide valuable insights into the structure and dynamics of the research landscape; however, certain limitations should be acknowledged. For example, the analysis was based solely on publication data from selected databases. Thus, relevant research disseminated through other channels, e.g., conference proceedings, institutional reports, and nonindexed journals, may have been excluded. In addition, other major databases, e.g., Web of Science and PubMed, were not included; thus, some relevant publications may have been omitted, potentially leading to partial coverage of the research field. Furthermore, although the bibliometric approach effectively identifies research trends and patterns, it does not reflect the qualitative depth or practical impact of individual studies beyond citation counts.
Conclusions
In this study, we performed a comprehensive bibliometric analysis of MC-based medical physics research in Korean institutions for the period 2015–2024. Based on the analysis of 310 studies published throughout the study period, we identified key publication trends, thematic research areas, and collaboration patterns.
A quantitative analysis of publication trends demonstrates consistent research productivity, with an increasing proportion of papers published in high-impact journals, suggesting a shift from quantity to quality to enhance scientific visibility and impact. In addition, the results of the thematic analysis revealed that researchers are focusing on radiation dosimetry and medical imaging, with the widespread use of MC simulation toolkits, e.g., Geant4 and MCNP, across various treatment modalities.
Active domestic collaboration, particularly between universities and hospitals, is a defining feature of this research environment; however, international collaboration remains limited, which indicates an opportunity to broaden global engagement. Furthermore, although AI has recently attracted global attention in radiotherapy research because of its potential to enable more advanced simulations, its application in Korean medical physics research is still at an early stage.
To maintain momentum and remain competitive in the evolving research landscape, Korean researchers must foster interdisciplinary collaborations, expand international networks, and embrace emerging technologies. These efforts are expected to help bridge the existing gaps in global research trends, improve treatment precision, and ensure the continued relevance and advancement of MC methodologies in modern medical practice.
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