Evidence map›Paper›PMID 38757006›Full record

ArticleHealth affairs scholar2024

Use of artificial intelligence and the future of peer review.

Howard Bauchner, Frederick P Rivara

Abstract read
In one paragraph

Article in Health affairs scholar, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Editorial: Research ethics and integrity in the artificial intelligence era.Frontiers in research metrics and analytics · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Retractions in Otolaryngology Publications.JAMA otolaryngology-- head & neck surgery · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Research Misconduct and Medical Journals.The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics · 2025
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Howard BauchnerBoston University Chobanian & Avedisian School of Medicine, Visiting Scholars Program, National University of Singapore, 02118, Singapore.
Frederick P RivaraDepartment of Pediatrics, University of Washington, Seattle, WA 98195, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conducting high-quality peer review of scientific manuscripts has become increasingly challenging. The substantial increase in the number of manuscripts, lack of a sufficient number of peer-reviewers, and questions related to effectiveness, fairness, and efficiency, require a different approach. Large-language models, 1 form of artificial intelligence (AI), have emerged as a new approach to help resolve many of the issues facing contemporary medicine and science. We believe AI should be used to assist in the triaging of manuscripts submitted for peer-review publication.

Indexed as

AIpeer reviewscientific communication

Identifiers

PMID38757006
PMCPMC11095530

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.