Peer Review of “Artificial Intelligence in Healthcare: 2023 Year in Review (Preprint)”
Bibliographic record
Abstract
Research question: The review [1] aims to understand the development and applications of artificial intelligence (AI) in health care, assessing the prevalence and impact of AI methods in biomedical research.The focus is on the frequency and types of publications in 2023, aiming to provide a comprehensive overview of the current AI landscape in health care and identify areas needing further research.It also aims to address the specialties in medicine with greater use of AI.• Research approach: The authors employed a mixed methods approach, combining classical bibliometric analysis and advanced deep learning techniques to analyze PubMed data.They established search criteria to collect papers published in 2023 related to AI and
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.207 | 0.149 |
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".