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Artificial Intelligence in Health Care: Bibliometric Analysis
Guo Y, Hao Z, Zhao S, Gong J, Yang F.. Journal of medical Internet research, 2020-07-29
Topic: Brain imaging, connectivity and assessment
Background As a critical driving power to promote health care, the health care-related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care-related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care-related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.
Key findings
- Results The search identified 5235 hits, of which 1473 publications were included in the analyses.
- Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019.
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Study details
- Study designlongitudinal study
- Effect size17.02%; 45.15%
- Evidence strengthModerate
How this study scored 60/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 35 of the 58 points we could judge it on, which is 60 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of medical Internet research) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32723713) |
| Free to read | 2 / 2 | Free to read |
| Points | 35 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 35 of the 58 points we could judge it on is 60 out of 100. |
| Total | 60 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020). Artificial Intelligence in Health Care: Bibliometric Analysis. Journal of medical Internet research. https://doi.org/10.2196/18228
Vancouver
Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial Intelligence in Health Care: Bibliometric Analysis. Journal of medical Internet research. 2020. doi:10.2196/18228. PMID: 32723713.
BibTeX
@article{guo2020-32723713,
author = {Guo, Y. and Hao, Z. and Zhao, S. and Gong, J. and Yang, F.},
title = {{Artificial Intelligence in Health Care: Bibliometric Analysis}},
journal = {Journal of medical Internet research},
year = {2020},
doi = {10.2196/18228},
pmid = {32723713},
url = {https://doi.org/10.2196/18228}
}
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