Artificial Intelligence and Medicine 2014-2024: Bibliometric Analysis and Global Impacts
Bibliographic record
Abstract
Artificial intelligence (AI) has radically transformed the field of medicine in the last decade, with a significant increase in academic publications. Based on 1783 English-language articles analyzed with Biblioshiny and VOSviewer tools, the findings highlight an annual growth rate of 30.38% and a significant increase from 2018 onwards. Each article received an average of 17.54 citations. The studies had contributions from 11678 authors and an international collaboration rate of 29%. There were single-author (118) and single-country (129) articles. Prominent contributing authors include Forestiero A, Mazzuca D and Zinno F. Harvard Medical School (104 papers) and the University of Toronto (83 papers) have played important roles in the advancement of AI applications in medicine. The USA stands out with both publication volume (3241 articles) and number of citations (9176). Journals such as Journal of Medical Internet Research (26 articles) and Frontiers in Medicine (25 articles) stand out as the leading publication venues in the field. The most cited articles were published in journals such as Jama, Radiology and Nature. This study highlights the wide-ranging applications of AI in areas such as machine learning, deep learning, natural language processing and computer vision, demonstrating its potential in medical imaging, genetic analysis and clinical decision support systems. Future research needs to focus on maintaining collaboration, increasing methodological rigor and finding solutions to emerging challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.091 | 0.177 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".