ReviewDigital health
Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Governance in Health Systems: Systematic Review of Frameworks and Integrative Model Proposal.Journal of medical Internet research · 2026Pooled it
- The rising tide of frailty in Parkinson's disease: a bibliometric study of global research landscape and emerging trends.Frontiers in neurologyPooled it
- Applications of Artificial Intelligence in the Health Sector: A PRISMA-Based Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence and Healthcare Policy: A Bibliometric Analysis of Global Research Trends.Healthcare (Basel, Switzerland) · 2026Article
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Bibliometric Mapping of 40 Years of AI in Nursing: Trends, Collaborations, and Research Hotspots Worldwide.Journal of nursing management · 2026Review
- Evaluating the Performance of AI Large Language Models in Detecting Pediatric Medication Errors Across Languages: A Comparative Study.Journal of clinical medicine · 2025Article
- Article
- Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role.Current psychiatry reports · 2024Review
- Artificial intelligence in primary health care: A bibliometric analysis of publications from 2015 to 2024.Digital healthReview
- Can artificial intelligence revolutionize healthcare in the Global South? A scoping review of opportunities and challenges.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.