Bibliometric exploration of artificial intelligence applications in healthcare: trends and future directions
Bibliographic record
Abstract
This research employs the PRISMA framework to conduct an extensive bibliometric analysis, delving into the dynamic realm of Artificial Intelligence (AI) within the healthcare domain. Spanning the years 2010 to 2023, the study systematically gathers and examines scholarly works to delineate the trends, patterns, and emerging topics about AI's integration into healthcare. A thorough initial screening yields substantial academic articles, conference papers, and reviews, forming the basis for analysis. The examination primarily focuses on quantifying publication patterns, identifying influential authors, institutions, and countries, and mapping the thematic landscape of AI in healthcare. Employing various bibliometric metrics such as publication trends, prolific authors, influential journals, and co-occurrence networks of keywords, the study uncovers the remarkable surge in research centred on AI-driven healthcare. This surge signifies a notable paradigm shift towards harnessing technology for predictive analytics, personalized medicine, and enhanced patient care. Additionally, by leveraging visualization tools like VOSviewer, the study presents informative graphical representations elucidating clusters and associations among keywords, thereby providing deeper insights into the interdisciplinary dimensions of AI in healthcare. This study provides a structured overview of the evolving landscape of AI in healthcare, providing valuable perspectives for researchers, practitioners, and policymakers aiming to harness the potential of AI for advancing healthcare delivery and outcomes. The implications of these findings underscore the transformative potential of AI technologies in revolutionizing healthcare delivery, promoting sustainable healthcare practices, and fostering innovative solutions for future challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.099 | 0.241 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".