Exploring the status of artificial intelligence for healthcare research in Africa: a bibliometric and thematic analysis
Bibliographic record
Abstract
Abstract This paper explores the status of Artificial Intelligence (AI) for healthcare research in Africa. The aim was to use bibliometric and thematic analysis methods to determine the publication counts, leading authors, top journals and publishers, most active institutions and countries, most cited institutions, funding bodies, top subject areas, co-occurrence of keywords and co-authorship. Bibliographic data were collected on April 9 2022, through the Lens database, based on the critical areas of authorship studies, such as authorship pattern, number of authors, etc. The findings showed that several channels were used to disseminate the publications, including articles, conference papers, reviews, and others. Publications on computer science topped the list of documented subject categories. The Annals of Tropical Medicine and Public Health is the top journal, where articles on AI have been published. One of the top nations that published AI research was the United Kingdom. With 143 publications, Harvard University was the higher education institution that produced the most in terms of affiliation. It was discovered that the Medical Research Council was one of the funding organizations that supported research, resulting in the publication of articles in AI. By summarizing the current research themes and trends, this work serves as a valuable resource for researchers, practitioners, and funding organizations interested in Artificial intelligence for healthcare research in Africa.
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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 | 0.030 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.141 | 0.186 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".