The application of Artificial Intelligence to medical education in the last decade - a bibliometric analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND As an important driving force leading a new round of scientific and technological revolution and industrial transformation, Artificial Intelligence (AI) has spawned a large number of new products, new technologies, new formats and new models, and also brought more possibilities for the modernization of medical education. Looking into the future, we should work together to build a high-quality and warm AI medical education ecology. How to make human-machine collaboration smarter and human-machine dialogue more friendly is a long-term topic of "AI + medical education" Education is dynamic and evolving, thinking rationally about the relationship between people and technology. Further promoting the deep integration and innovative development of AI and medical education can better empower the modernization of education. OBJECTIVE Use CiteSpace and VOSviewer to determine the current and emerging trends in Artificial Intelligence on medical education from 2013 to 2022. METHODS Search the literatures related to Artificial Intelligence on medical education in Web of Science core database from 2013 to 2022. Use CiteSpace and VOS viewers to analyze countries, institutions, authors, references and keywords that meet the requirements. RESULTS We identified 195 articles about Artificial Intelligence on medical education from 2013 to 2022 and found that the annual incidence rate increased over time. The most active country was the United States, the most active institution was the Harvard Med Sch and Univ Toronto. And Bissonnette, Vincent; Blacketer, Charlotte; Del Maestro, Rolando f; Ledows, Nicole; Mirchi, Nykan; Winkler-schwartz, Alexander; Yilamaz, Recai was the leading authors. Besides, “Medical students' attitude towards Artificial Intelligence: a multicentre survey” was the largest number of citation papers. References and keyword analysis showed that “radiology”, “medical physics” ,“ehealth” ,“surgery”,and “specialty” were the focus of these studies, while “big data”, and “management” were the frontiers of research. CONCLUSIONS The bibliometric study shows that the research of Artificial Intelligence on medical education is a promising research field. The current research is mainly focused on a few aspects of medical education, with the progress of technology, it is expected to broaden the direction in the future. At present, the urgent problem to be solved is to strengthen inter-regional cooperation and improve the quality of research. Our findings provide valuable information for researchers to determine a better perspective and develop future research directions.
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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 | 0.010 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.158 | 0.295 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".