Submitted by Marianna Shershneva on

Wang S, Yang L, Li M, Zhang X, Tai X. Medical education and artificial intelligence: Web of Science-based bibliometric analysis (2013-2022). JMIR Med Educ. 2024;10:e51411. doi: 10.2196/51411.

Abstract

Background: Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted. Objective: This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education. Methods: Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer. Results: A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers. Conclusions: The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

Questions

  1. Based on the trends identified in the article, how is artificial intelligence (AI) being integrated into medical education? What are the potential benefits and challenges of using AI in training future healthcare professionals?
  2. What do you think of a bibliometric analysis used by the authors? Did this method effectively capture the trends and key insights? Are there any limitations or biases in their approach that should be considered?
  3. Based on the article’s findings, what are the emerging areas of interest in medical education and AI that you believe should receive more attention?

Comments

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SprunkiRetake Sprunki replied on

Witnessing the rapid growth of AI in medical training firsthand, it is validating to see the data confirm that fields like radiology are leading this technological shift, though the disparity in global collaboration remains a concern. Ideally, integrating these complex tools into our daily curriculum would be as intuitive and engaging as the drag-and-drop mechanics of Sprunki Retake, allowing us to focus on the "music" of patient care rather than the technical noise. It is clear that while the technology is advancing, we need better management strategies to ensure these resources are effectively shared across institutions.

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SoundboardW Com replied on

From a reader’s perspective, the rise of AI in radiology, surgery, and eHealth shows how adaptive learning systems and data-driven simulations are reshaping training. Benefits include personalized feedback and clinical decision support, but data bias and overreliance remain concerns. The bibliometric method maps trends well, though Web of Science limits scope. Emerging focuses like big data governance deserve attention—much like curating a precise soundboard at https://soundboardw.com/

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Junior Wiley replied on

one takeaway for me is that “transparency” in ai for hpe isn’t a single switch; it’s about being clear on purpose, data provenance, and who is accountable when outputs are wrong. i don’t think every educator needs to understand the math, but we do need enough literacy to ask what data trained it, what biases were tested, and what evidence supports its use in assessment. in practice i’d push for an ai use statement in every course and a human-override pathway for any ai-supported decision affecting progression. i’ve also seen how easy it is for irrelevant links to creep into student-facing spaces, and i had to remove FNF from a resource list because it undermined trust in the rest of our guidance.

    Junior Wiley replied on