Global Research of Medical Technology Management: A Bibliometric Analysis
Bibliographic record
Abstract
OBJECTIVES: To explore potential keywords, research clusters, collaborative pattern, and research trends in the field of medical technology management (MTM) through bibliometric analysis, providing insights for researchers, policy makers, and hospital administrators. METHODS: A retrieval formula was applied to the title, abstract, and keywords in the Web of Science (WoS) Core Collection, along with system-recommended terms, to identify articles on MTM. A total of 181 articles published between 1974 and 2022 were retained for quantitative analysis. The global trend of research output; total citations, average citations, and H-index; and bibliographic coupling, co-authorship, and keyword co-occurrence were analyzed using VOSviewer. RESULTS: The number of articles on MTM has been steadily increasing year by year. The focus of research has shifted from addressing basic medical needs to prioritizing emergency response and medical information security. The United States, Italy, and the United Kingdom emerged as the main contributors, with the United States leading in both volume of publications (60 articles) and academic impact (H-index = 21). Authors from the United Kingdom and the United States led the way in cross-border cooperation. The top five institutions, ranked by total link strength among cross-institutional authors, were primarily located in Canada and Spain. CONCLUSIONS: The field of MTM has experienced stable growth over the past three decades (1993-2022). The shift of research focus has prompted a heightened emphasis on protecting patient privacy and ensuring the security of medical data. Future research should emphasize interdisciplinary and professional collaboration, as well as international cooperation and open sharing of knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.069 | 0.482 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".