ArticleCureus2025
Can AI Tools Reliably and Effectively Detect Plagiarism in Scientific Writing?
Article in Cureus, 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.
- Disclosure is not documentation: an open science framework for documenting generative AI use in scholarly research and publication workflows.Research integrity and peer review · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and aim Artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT (San Francisco, CA: OpenAI) and Bard (now Gemini) (Mountain View, CA: Google), is increasingly used in scientific writing. However, its rapid adoption has raised ethical concerns, especially regarding plagiarism. Current standards of plagiarism detection for scientific writing require a costly and laborious process, done by journal reviewers, sparking questions of whether free LLM tools could streamline the process. Further, AI-generated text can closely resemble genuine scientific writing, raising questions about authenticity and detection. Although various tools exist to identify AI-generated content, their effectiveness remains uncertain. The objective of this study is to evaluate the ability of free online AI tools to identify plagiarism in scientific papers. Methods The following three topics were used for this study: 3D evaluation of the anterior mandible, low-dose cone beam CT, and ameloblastoma case reports. Plagiarized "mashup" papers were created by combining paragraphs from published papers - two papers on 3D evaluation, three on low-dose CBCT, and two on ameloblastoma, each with six paragraphs. These mashups were tested for plagiarism using ChatGPT 3.5 and Bard (five times each) and SmallSEO (London, UK: SmallSEO Tools) (three times). ChatGPT and Bard were then prompted to rewrite the plagiarized mashups, and the rewrites were retested for plagiarism and evaluated for AI detection. Results ChatGPT was unable to identify plagiarism (0/15). Bard detected plagiarism in 8/15 trials, but never identified all plagiarized text. SmallSEO identified 100% of the plagiarism and correctly sourced it, but after AI rewrites, SmallSEO missed 87/90 plagiarized paragraphs. Neither AI-detection tool could definitively detect AI-generated rewrites, with likelihoods never exceeding 70%. ChatGPT and Bard were unable to reliably detect plagiarism. AI-rewritten content was undetectable by plagiarism checkers, and AI-detection tools could not definitively identify AI-generated text. Conclusion ChatGPT, Bard, and SmallSEO are currently unable to identify plagiarism in scientific text. Further, these generative AI tools are capable of rewriting plagiarized text to evade plagiarism detection. Finally, AI-detection tools cannot reliably detect the use of AI in AI-rewritten text.
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.