The study employed two of the most widely used bibliometric mapping tools in science: CiteSpace and VOSviewer. These software platforms allow researchers to transform vast bibliographic databases into visual networks, revealing patterns that would be invisible to anyone reading papers one at a time. CiteSpace, developed originally by Chaomei Chen, excels at detecting bursts of activity in research fronts, identifying pivotal papers that bridge otherwise disconnected bodies of literature, and tracing how keywords and concepts evolve over time. VOSviewer, created by Nees Jan van Eck and Ludo Waltman at Leiden University, constructs similarity-based maps in which publications, authors, or keywords are clustered according to how frequently they cite one another or co-occur in the same documents. By applying both tools to the medical education literature, the team could triangulate their findings, using one platform’s strengths to compensate for the other’s blind spots and thereby producing a more robust picture of the field’s intellectual architecture.

The timing of the analysis is significant. The researchers deliberately framed their window as the past decade, capturing the period in which digital technologies matured from novelty to necessity. Their results show a steady increase in publications throughout the period, but the most striking feature is the surge from 2022 to 2024, which the authors attribute to two converging forces: rapid technological advancement and the COVID-19 pandemic. When the pandemic forced medical schools worldwide to suspend in-person teaching, clinical rotations, and laboratory sessions almost overnight, digital tools stopped being optional supplements and became the only viable channel for education. That forced adoption, the analysis suggests, permanently altered the trajectory of the field, converting temporary workarounds into lasting infrastructure and triggering a wave of research into how well these tools actually work.

Among the key themes the mapping revealed, artificial intelligence-powered personalization stands out as perhaps the most consequential. Adaptive learning platforms, guided by algorithms that adjust content difficulty and pacing to individual student performance, promise to replace the one-size-fits-all lecture model with something closer to a personal tutor for every learner. The bibliometric data show this theme intersecting with work on big data analytics, in which student interaction data feeds back into the system to refine recommendations continuously. Closely related is the theme of deep learning applied to medical imaging, a field that has transformed both clinical practice and the training of future radiologists. Convolutional neural networks can now detect diseases in retinal photographs, chest radiographs, and pathology slides with performance that in some benchmarks rivals experienced clinicians, and medical schools are increasingly expected to teach students how these systems work, what their limitations are, and how to interpret their outputs responsibly.

Virtual reality and haptic simulation form another major cluster in the research landscape. The analysis identified a rich literature on VR-based surgical training, including simulations of cerebral aneurysm clipping with real-time haptic feedback, endodontic microsurgery rehearsal, and smart haptic gloves that let trainees feel resistance as they practice procedures such as external ventricular drain placement. Systematic reviews cited in the study support the educational validity of virtual haptics in surgical simulation, and meta-analyses of virtual patient simulations in health professions education indicate meaningful learning gains. The pandemic accelerated adoption here as well, with studies documenting how virtual and augmented reality sustained the quality of medical education when cadaver laboratories and operating theaters were off limits. Anatomy education, long dependent on physical dissection, has proven a particularly fertile ground for hybrid models that combine virtual environments with traditional laboratory experiences.

Perhaps no theme has generated more attention, or more controversy, than the arrival of large language models. The bibliometric analysis captured the sudden eruption of research on tools such as ChatGPT following their public release, including studies evaluating their performance on the United States Medical Licensing Examination and the Japanese medical licensing examination. Other cited work compared physician and AI chatbot responses to patient questions posted on public forums, examined ChatGPT-assisted teaching in pediatric clinical skills training, and explored language model-powered simulated patients that give medical students automated feedback on their history-taking technique. These studies suggest that generative AI can serve as an interactive teaching partner, offering on-demand explanation, case-based reasoning practice, and round-the-clock availability that human faculty cannot match. Yet the same literature is candid about the pitfalls: models can hallucinate clinical facts, exhibit biases embedded in their training data, and potentially encourage shortcuts that undermine deep learning if students lean on them uncritically.

The analysis also surfaced the structural inequalities that shadow this technological optimism. The researchers highlight disparities in global research capacity, noting that the literature is dominated by contributions from a limited set of countries and institutions while many regions remain marginal participants in shaping the field. Data privacy concerns, ethical questions surrounding algorithmic decision-making, and resource inequality compound the problem. Advanced VR suites and AI platforms are expensive, and institutions in low-resource settings often cannot afford them, raising the prospect that digital education could widen rather than close existing gaps in medical training quality. The cited literature on simulation in low-resource settings and on blockchain-based platforms for secure data sharing reflects early attempts to address these challenges, but the bibliometric evidence suggests such work remains a small fraction of the overall output.

What makes the study’s conclusions notable is their balance. The authors conclude that intelligent digital platforms have been genuinely transformative, particularly in clinical training, adaptive learning, and medical diagnostics simulation, and that these technologies have the potential to revolutionize medical education. At the same time, they insist that realizing that potential equitably requires confronting ethical, technical, and resource-based challenges head-on. Their recommendations for future research are concrete: foster international collaboration so that expertise and infrastructure flow across borders, develop standardized frameworks for evaluating and integrating digital tools into curricula, and create inclusive, low-cost digital tools that can democratize access to high-quality medical education rather than concentrating it among wealthy institutions.

The broader significance of the work lies in its method as much as its findings. Bibliometric mapping of this kind gives educators, funders, and policymakers an evidence-based view of where a fast-moving field has been and where it is heading, replacing anecdote and enthusiasm with measurable trends. As medical schools everywhere grapple with how to train doctors for an era in which AI assists diagnosis, VR simulates surgery, and chatbots answer clinical questions, studies like this one provide the cartography needed to navigate deliberately rather than drift. The decade from 2015 to 2024 turned digital and intelligence education in medicine from a niche interest into a global research enterprise; the coming decade will determine whether its benefits reach every medical student, or only a fortunate few.

Subject of Research: Bibliometric analysis of digital and artificial intelligence technologies in medical education from 2015 to 2024

Article Title: Digital and Intelligence Education in Medicine: A Bibliometric and Visualization Analysis Using CiteSpace and VOSviewer

Article References: Yang, B. X., Zhou, F., Bai, N., Zhou, S., Luo, C., Wang, Q., Wong, A. K. C., & Lin, F. (2025). Digital and Intelligence Education in Medicine: A Bibliometric and Visualization Analysis Using CiteSpace and VOSviewer. Frontiers of Digital Education, 2(1), Article 10. https://doi.org/10.1007/s44366-025-0046-y

Image Credits: AI Generated

DOI: 10.1007/s44366-025-0046-y

Keywords: medical education, artificial intelligence, bibliometrics, CiteSpace, VOSviewer, virtual reality, large language models, deep learning, adaptive learning, simulation, COVID-19, digital health