ArticleAdvances in simulation (London, England)2025
Ability of AI detection tools and humans to accurately identify different forms of AI-generated written content.
Article in Advances in simulation (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- The Em Dash-Should We Allow AI to Change Our Writing Styles?Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe increasing use of artificial intelligence (AI) by scholars presents a pressing challenge to healthcare publishing. While legitimate use can potentially accelerate scholarship, unethical approaches also exist, leading to factually inaccurate and biased text that may degrade scholarship. Numerous online AI detection tools exist that provide a percentage score of AI use. These can assist authors and editors in navigating this landscape. In this study, we compared the scores from three AI detection tools (ZeroGPT, PhraslyAI, and Grammarly AI Detector) across five plausible conditions of AI use and evaluated them against human assessments.
methodsThirty open access articles published in the journals Advances in Simulation and Simulation in Healthcare prior to 2022 were selected, and the article introductions were extracted. Five experimental conditions were examined, including: (1) 100% human written; (2) human written, light AI editing; (3) human written, heavy AI editing; (4) AI written text from human content; and (5) 100% AI written from article title. The resulting materials were assessed by three open-access AI detection tools and five blinded human raters. Results were summarized descriptively and compared using repeated measures analysis of variance (ANOVA), intraclass correlation coefficients (ICC), and Bland-Altman plots.
resultsThe three AI detection tools were able to differentiate between the five test conditions (p < 0.001 for all), but varied significantly in absolute score, with ICC ranging from 0.57 to 0.95, raising concerns regarding overall reliability of these tools. Human scoring was far less consistent, with an overall accuracy of 19%, indistinguishable from chance.
conclusionWhile existing AI detection tools can meaningfully distinguish plausible AI use conditions, reliability across these tools is variable. Human scoring accuracy is uniformly low. Use of AI detection tools by scholars and journal editors may assist in determining potentially unethical use but they should not be relied upon alone at this time.
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.