Global Trends in mHealth and Medical Education Research: Bibliometrics and Knowledge Graph Analysis
Open accessAbstract
Background: Mobile health (mHealth) is an emerging mobile communication and networking technology for health care systems. The integration of mHealth in medical education is growing extremely rapidly, bringing new changes to the field. However, no study has analyzed the publication and research trends occurring in both mHealth and medical education. Objective: The aim of this study was to summarize the current application and development trends of mHealth in medical education by searching and analyzing published articles related to both mHealth and medical education. Methods: The literature related to mHealth and medical education published from 2003 to 2023 was searched in the Web of Science core database, and 790 articles were screened according to the search strategy. The HistCite Pro 2.0 tool was used to analyze bibliometric indicators. VOSviewer, Pajek64, and SCImago Graphica software were used to visualize research trends and identify hot spots in the field. Results: In the past two decades, the number of published papers on mHealth in medical education has gradually increased, from only 3 papers in 2003 to 130 in 2022; this increase became particularly evident in 2007. The global citation score was determined to be 10,600, with an average of 13.42 citations per article. The local citation score was 96. The United States is the country with the most widespread application of mHealth in medical education, and most of the institutions conducting in-depth research in this field are also located in the United States, closely followed by China and the United Kingdom. Based on current trends, global coauthorship and research exchange will likely continue to expand. Among the research journals publishing in this joint field, journals published by JMIR Publications have an absolute advantage. A total of 105 keywords were identified, which were divided into five categories pointing to different research directions. Conclusions: Under the influence of COVID-19, along with the popularization of smartphones and modern communication technology, the field of combining mHealth and medical education has become a more popular research direction. The concept and application of digital health will be promoted in future developments of medical education.
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Knowledge graph technology has been increasingly applied in education to represent complex knowledge systems through structured networks.
“In parallel, knowledge graph technology has been increasingly applied in education to represent complex knowledge systems through structured networks [12, 13].”
From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [13]
The Methods (Data Analysis and Visualization; page unavailable) describe knowledge mapping used to analyze research on mHealth and medical education. The Results (Keyword Co-Occurrence and Research Trends; page unavailable) show a network of research keywords, not knowledge graphs used in education to represent complex knowledge systems. Searches for educational knowledge graphs and structured networks found no passage addressing that claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe results highlight the importance of combining AI technologies with structured knowledge representation tools, such as knowledge graphs.
“Second, the results highlight the importance of combining AI technologies with structured knowledge representation tools, such as knowledge graphs [12, 13, 14].”
From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [13]
The Methods section describes knowledge mapping with VOSviewer, and the Results section mentions AI as an example of a digital-health technology. Neither passage addresses combining AI with knowledge graphs or says the results highlight the importance of doing so. Searched for knowledge graphs and AI integration; page numbers were not available in our copy. (LLM, conf=0.76)
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Cites
Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine
Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., Perez, J. L., Pérez Marc, G., Moreira, E. D., Zerbini, C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2034577 · Cited by 14,592 · cited 1× · see citing works
“Additionally, there were peaks in the LCS detected in 2008 (7), 2013 (14), 2014 (14), and 2016 (15), indicating that studies in these years had large contributions to the research published in this field in the subsequent years.”
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Flagged first, then by citations
Research works printed but not linked: 17
- [1]Akter S Ray P mHealth - an ultimate platform to serve the unserved Yearb Med Inform 2010 94 100 doi 10.1055/s-0038-1638697 Medline 20938579 · DOI
- [2]Tachakra S Wang XH Istepanian RSH Song YH Mobile e-health: the unwired evolution of telemedicine Telemed J E Health 2003 9 3 247 257 doi 10.1089/153056203322502632 Medline 14611692 · DOI
- [3]Laxminarayan S Istepanian RS UNWIRED E-MED: the next generation of wireless and internet telemedicine systems IEEE Trans Inf Technol Biomed Sep 2000 4 3 189 193 doi 10.1109/titb.2000.5956074 Medline 11026588 · DOI
- [4]Silva BMC Rodrigues JJPC de la Torre Díez I López-Coronado M Saleem K Mobile-health: a review of current state in 2015 J Biomed Inform Aug 2015 56 265 272 doi 10.1016/j.jbi.2015.06.003 Medline 26071682 · DOI
- [5]Ellegaard O Wallin JA The bibliometric analysis of scholarly production: how great is the impact? Scientometrics 2015 105 3 1809 1831 doi 10.1007/s11192-015-1645-z Medline 26594073 PMC4643120 · DOI
- [6]Glanville J Kendrick T McNally R Campbell J Hobbs FDR Research output on primary care in Australia, Canada, Germany, the Netherlands, the United Kingdom, and the United States: bibliometric analysis BMJ Mar 8 2011 342 d1028 doi 10.1136/bmj.d1028 Medline 21385804 PMC3050436 · DOI
- [7]Chen C Dubin R Kim MC Emerging trends and new developments in regenerative medicine: a scientometric update (2000 – 2014) Expert Opin Biol Ther Sep 2014 14 9 1295 1317 doi 10.1517/14712598.2014.920813 Medline 25077605 · DOI
- [8]Istepanian RSH Lacal JC Emerging mobile communication technologies for health: some imperative notes on m-health Presented at 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Sep 17 to 21, 2023: 1414 1416 Cancun, Mexico doi 10.1109/IEMBS.2003.1279581 · DOI
- [9]Chen C Searching for intellectual turning points: progressive knowledge domain visualization Proc Natl Acad Sci U S A Apr 6 2004 101 Suppl 1 5303 5310 doi 10.1073/pnas.0307513100 Medline 14724295 PMC387312 · DOI · Paper page
- [10]Chen C Hu Z Liu S Tseng H Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace Expert Opin Biol Ther 05 2012 12 5 593 608 doi 10.1517/14712598.2012.674507 Medline 22443895 · DOI
- [11]Zhang J Luo Z Zhang R et al The transition of surgical simulation training and its learning curve: a bibliometric analysis from 2000 to 2023 Int J Surg 05 9 2024 doi 10.1097/JS9.0000000000001579 Medline 38729115 PMC11175803 · DOI
- [12]Payne KFB Wharrad H Watts K Smartphone and medical related app use among medical students and junior doctors in the United Kingdom (UK): a regional survey BMC Med Inform Decis Mak Oct 30 2012 12 121 doi 10.1186/1472-6947-12-121 Medline 23110712 PMC3504572 · DOI
- [13]Mahase E Covid-19: WHO declares pandemic because of “alarming levels” of spread, severity, and inaction BMJ Mar 12 2020 368 m1036 doi 10.1136/bmj.m1036 Medline 32165426 · DOI
- [15]Wang C Horby PW Hayden FG Gao GF A novel coronavirus outbreak of global health concern Lancet Feb 15 2020 395 10223 470 473 doi 10.1016/S0140-6736(20)30185-9 Medline 31986257 PMC7135038 · DOI · Paper page
- [16]Muntz MD Franco J Ferguson CC Ark TK Kalet A Telehealth and medical student education in the time of COVID-19-and beyond Acad Med Dec 1 2021 96 12 1655 1659 doi 10.1097/ACM.0000000000004014 Medline 35134026 PMC8603435 · DOI
- [17]Simkó M Mattsson MO 5G wireless communication and health effects–a pragmatic review based on available studies regarding 6 to 100 GHz Int J Environ Res Public Health Sep 13 2019 16 18 3406 doi 10.3390/ijerph16183406 Medline 31540320 PMC6765906 · DOI · Paper page
- [18]Journal citation reports Clarivate URL https://clarivate.com/products/scientific-and-academic-research/research-analytics-evaluation-and-management-solutions/journal-citation-reports/ Accessed 24-05-2024
Cited by
Sorted by citations
Zhao, C., Zhu, J., Liu, J., Zhao, W., & Pang, Y. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09469-0 · Cited by 1 · see citing works
“In parallel, knowledge graph technology has been increasingly applied in education to represent complex knowledge systems through structured networks [12, 13].”
- Authors
- He, Y., Xie, Z., Li, J., Meng, Z., Xue, D., & Hao, C.
- Affiliations
- Harbin Medical University (CN)
- Corresponding author
- Hao, Chenjun
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2024-06-04
- Volume
- 10
- Pages
- e52461-e52461
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC11185284
- OpenAlex
- W4399401374
- Cited by
- 9
- References
- 1
- Citations by year
- 2026: 5 · 2025: 4
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- mobile health, medical education, research trends, digital health, smartphones, COVID-19 pandemic, research collaboration, citation analysis, knowledge graphs
- MeSH terms
- COVID-19, Education, Medical, Humans, Bibliometrics, Telemedicine
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