Identification of Key Nodes and Global Research Trends of artificial intelligence (AI)/ Large Language Model (LLM) in Medical Education: A Bibliometric Analysis(1986-2024) (Preprint)
Notice bibliographique
Résumé
BACKGROUND In recent years, artificial intelligence (AI)/ Large Language Model(LLM) has significantly transformed the field of medical education, prompting extensive research. Many important bibliometric nodes in this emerging field have yet to be explored. OBJECTIVE This study aims to synthesize diverse publications to analyze the bibliometric attributes of this field, including landmarks, emerging topics, and development status. Also, the formation pattern of bibliometrics in emerging fields will be analyzed simultaneously. METHODS We utilized the Web of Science Core Collection to download literature on AI in medical education. Bibliometric analysis was performed using Citespace v.6.3.R1 software, which facilitated the analysis of publication volume, collaboration within the field, citation networks, and keyword analysis. Additionally, we employed the Bibliometrix package based on R for generating conceptual and thematic maps related to the topic. RESULTS A total of 547 publications were retrieved from the Web of Science Core Collection, covering the period from 1986 to 2024. The five leading countries in terms of publication volume were the United States, England, China, Canada, and India. The most prolific journals included JMIR Medical Education, BMC Medical Education, Cureus Journal of Medical Science, Medical Teacher, and Academic Medicine. The top institutions contributing to this body of work were the University of London, National University of Singapore, Harvard University, and Stanford University. Other important bibliometric characteristics, such as high-yield authors, highly cited authors, and frequently collaborating authors, were also identified. A citation co-citation network was established to determine the key knowledge base and potential pivotal literature in the field. Citespace software was utilized to identify clusters and bursts of high-frequency terms, highlighting current hotspots within the discipline. The Bibliometrix toolkit provided conceptual and thematic maps to assess the development status and trends in the field. CONCLUSIONS AI/LLM in medical education has emerged as a burgeoning field in recent years. JMIR Medical Education was identified as a key node based on its notable bibliometric characteristics. In the early stages of this emerging discipline, the journal's submission calls significantly influence the bibliometric features of the literature, thereby promoting field development. The discipline is currently in a developmental phase, lacking well-defined subfields. Topics such as "nursing education," "digital health," "medical exams," and "conversational agents" have garnered increasing interest over time. Research related to ChatGPT and large language models appears to occupy a central and influential position. Furthermore, medical ethics, medical training, and skills training are emerging focuses of current development and innovation, particularly in gene technology. However, this analysis indicates that there has been insufficient attention given to clinical reasoning, undergraduate education, and virtual reality in the context of AI/LLM in medical education.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,049 | 0,091 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».