ArticlePloS one2017
Artificial intelligence in peer review: How can evolutionary computation support journal editors?
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Evolution of manuscript peer review: expanding roles in exercise rehabilitation research.Journal of exercise rehabilitation · 2026Article
- AI-assisted peer review: efficiency for reviewers, burden for authors-a narrative review.Frontiers in artificial intelligence · 2026Review
- AI in peer review: can artificial intelligence be an ally in reducing gender and geographical gaps in peer review? A randomized trial.Research integrity and peer review · 2025Article
- A Critical Appraisal of Reporting in Randomized Controlled Trials Investigating Osteopathic Manipulative Treatment: A Meta-Research Study.Journal of clinical medicine · 2024Review
- Recent Trend in Artificial Intelligence-Assisted Biomedical Publishing: A Quantitative Bibliometric Analysis.Cureus · 2023Review
- An Improved Peer-Review System to Compensate for Scientific Misconduct in Health-Sensitive Topics.Public health reviews · 2023Article
- Speeding up to keep up: exploring the use of AI in the research process.AI & society · 2022Article
- Seasonal Entropy, Diversity and Inequality Measures of Submitted and Accepted Papers Distributions in Peer-Reviewed Journals.Entropy (Basel, Switzerland) · 2019Article
- A scoping review of simulation models of peer review.Scientometrics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors' workloads, are treated as trade secrets by publishing houses and are not shared publicly. To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies. The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy). Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.