ArticleThe Journal of general physiology2024
The artificial intelligence revolution...in unethical publishing: Will AI worsen our dysfunctional publishing system?
Article in The Journal of general physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Strengthening Australia's research integrity system.Internal medicine journal · 2026Article
- Can artificial intelligence create human touch in medical writing? A pilot study.Croatian medical journal · 2026Article
- Artificial Intelligence in Medical Writing: Is It an Exception to Evidence-Based Medicine?JMA journal · 2026Article
- Review
- Unwanted scenario of medical journals' future: Artificial intelligence and medical writing.Arquivos brasileiros de oftalmologia · 2025Article
- What happened to the pursuit of truth?The Journal of general physiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Scholarly publishing has been shaped by the pressure of a liquid economy to become an exercise in branding more than a vehicle for the advancement of science. The current revolution in artificial intelligence (AI) is poised to make matters worse. The new generation of large language models (LLMs) have shown impressive capabilities in text generation and are already being used to write papers, grants, peer review reports, code for analyses, and even perform literature reviews. Although these models can be used in positive ways, the metrics and pressures of academia, along with our dysfunctional publishing system, stimulate their indiscriminate and uncritical use to speed up research outputs. Thus, LLMs are likely to amplify the worst incentives of academia, greatly increasing the volume of scientific literature while diluting its quality. At present, no effective solutions are evident to overcome this grim scenario, and nothing short of a cultural revolution within academia will be needed to realign the practice of science with its traditional ideal of a rigorous search for truth.
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.