Evidence map›Paper›PMID 39546778›Full record

ArticleJournal of medical Internet research2024

Decoding the Digital Pulse: Bibliometric Analysis of 25 Years in Digital Health Research Through the Journal of Medical Internet Research.

Robert Kaczmarczyk, Theresa Isabelle Wilhelm, Jonas Roos, Ron Martin

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025
    Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Robert KaczmarczykDepartment of Dermatology and Allergy, Technical University of Munich, Munich, Germany.ORCID 0000-0002-8570-1601
Theresa Isabelle WilhelmEye Center-Medical Center, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Freiburg, Germany.ORCID 0000-0002-7462-1868
Jonas RoosDepartment of Orthopedics and Trauma Surgery, University Hospital of Bonn, Bonn, Germany.ORCID 0000-0001-8843-4695
Ron MartinDepartment of Plastic and Hand Surgery, Burn Care Center, BG Klinikum Bergmannstrost Halle, Halle, Germany.ORCID 0009-0000-0248-537X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the digital health landscape continues to evolve, analyzing the progress and direction of the field can yield valuable insights. The Journal of Medical Internet Research (JMIR) has been at the forefront of disseminating digital health research since 1999. A comprehensive network analysis of JMIR publications can help illuminate the evolution and trends in digital medicine over the past 25 years.

objectiveThis study aims to conduct a detailed network analysis of JMIR's publications to uncover the growth patterns, dominant themes, and potential future trajectories in digital health research.

methodsWe retrieved 8068 JMIR papers from PubMed using the Biopython library. Keyword metrics were assessed using accuracy, recall, and F

resultsJMIR's publication volume showed consistent growth, with a peak in 2020. The United States dominated country contributions, with China showing a notable increase in recent years. Keyword analysis from 1999 to 2023 showed significant thematic shifts, from an early internet and digital health focus to the dominance of COVID-19 and advanced technologies such as machine learning. Predictions for 2024-2026 suggest an increased focus on artificial intelligence, digital health, and mental health.

conclusionsNetwork analysis of JMIR publications provides a macroscopic view of the evolution of the digital health field. The journal's trajectory reflects broader technological advances and shifting research priorities, including the impact of the COVID-19 pandemic. The predicted trends underscore the growing importance of computational technology in future health care research and practice. The findings from JMIR provide a glimpse into the future of digital medicine, suggesting a robust integration of artificial intelligence and continued emphasis on mental health in the postpandemic era.

Indexed as

BibliometricsCOVID-19Digital HealthHumansInternetNatural Language ProcessingPandemicsPeriodicals as TopicSARS-CoV-2TelemedicineAIalgorithmanalyticsartificial intelligenceClaude 3 Opusdeep learningdigitaldigital healthdigital interventiondigital technologyeHealthJMIR publication analysislarge language modelsmachine learningmobile phonemodelnatural language processingnetwork analysisNLPpandemicpostpandemic erapractical model

Identifiers

PMID39546778
PMCPMC11607559

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.