ArticleThe archives of bone and joint surgery2025
From Algorithms to Academia: An Endeavor to Benchmark AI-Generated Scientific Papers against Human Standards.
Article in The archives of bone and joint surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Objectives: The aim of this study is to quantitatively investigate the accuracy of text generated by AI large language models while comparing their readability and likelihood of being accepted to a scientific compared to human-authored papers on the same topics. Methods: The study consisted of two papers written by ChatGPT, two papers written by Assistant by scite, and two papers written by humans. A total of six independent reviewers were blinded to the authorship of each paper and assigned a grade to each subsection on a scale of 1 to 4. Additionally, each reviewer was asked to guess if the paper was written by a human or AI and explain their reasoning. The study authors also graded each AI-generated paper based on factual accuracy of the claims and citations. Results: The human-written calcaneus fracture paper received the highest score of a 3.70/4, followed by Assistant-written calcaneus fracture paper (3.02/4), human-written ankle osteoarthritis paper (2.98/4), ChatGPT calcaneus fracture (2.89/4), ChatGPT Ankle Osteoarthritis (2.87/4), and Assistant Ankle Osteoarthritis (2.78/4). The human calcaneus fracture paper received a statistically significant higher rating than the ChatGPT calcaneus fracture paper (P = 0.028) and the Assistant calcaneus fracture paper (P = 0.043). The ChatGPT osteoarthritis review showed 100% factual accuracy, the ChatGPT calcaneus fracture review was 97.46% factually accurate, the Assistant calcaneus fracture was 95.56% accurate, and the Assistant ankle osteoarthritis was 94.98% accurate. Regarding citations, the ChatGPT ankle osteoarthritis paper was 90% accurate, the ChatGPT calcaneus fracture was 69.23% accurate, the Assistant ankle osteoarthritis was 35.14% accurate, and the Assistant calcaneus fracture was 39.68% accurate. Conclusion: Through this paper we emphasize that while AI holds the promise of enhancing knowledge sharing, it must be used responsibly and in conjunction with comprehensive fact-checking procedures to maintain the integrity of the scientific discourse.
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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.