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Citation integrity in the age of AI: evaluating the risks of reference hallucination in maxillofacial literature.
Abstract
The increasing adoption of large language models (LLMs) such as ChatGPT in academic writing has introduced both opportunities and risks. While these tools enhance productivity and accessibility, their reliability in generating accurate references remains uncertain. This short communication highlights the growing concern of 'reference hallucination', where AI-generated citations appear legitimate but are fabricated or contain significant metadata errors. Across all scientific disciplines, including oral and maxillofacial surgery (OMFS), where evidence-based practice is foundational, such inaccuracies can undermine academic integrity and clinical trust. This article summarizes common reference-related errors reported in literature and calls for heightened editorial vigilance, AI-literacy training, and the integration of real-time bibliographic tools. Responsible use of AI in scholarly publishing is essential to preserving the quality and credibility of surgical literature.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The existing literature lacks comparative evidence because many of the studies examine a narrow slice of available models under non-comparable conditions [6,7].”
From How LLMs Cite and Why It Matters: A Cross-Model Audit of Reference Fabrication in AI-Assisted Academic Writing and Methods to Detect Phantom Citations (2026) · cites this paper as [6]
Citations
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 7 · Cited by 10
Research works printed but not linked: 4
- Flanagin, 2023, Nonhuman “authors” and implications for the integrity of scientific publication and medical knowledge, JAMA, 329, 637, 10.1001/jama.2023.1344 · DOI · Paper page
- Gao, 2023, Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers, npj Digit. Med., 6, 75, 10.1038/s41746-023-00819-6 · DOI · Paper page
- Kung, 2023, Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models, PLOS Digit Health, 2, 10.1371/journal.pdig.0000198 · DOI · Paper page
- Singhal, 2023, Large language models encode clinical knowledge, Nature, 620, 172, 10.1038/s41586-023-06291-2 · DOI · Paper page
Metadata
- Authors
- Jain, A., Nimonkar, P., & Jadhav, P.
- Affiliations
- Dr. D.Y. Patil Vidyapeeth, Pune (IN)
- Government Medical College (IN)
- Corresponding author
- Jain, Anuj
- Venue
- Journal of Cranio-Maxillofacial Surgery
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 53
- Issue
- 10
- Pages
- 1871-1872
- ISSN
- 1010-5182 (ISSN-L)
- OpenAlex
- W4413325295
- Cited by
- 10
- References
- 7
- Citations by year
- 2026: 8 · 2025: 2
- FWCI
- 10.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cranio-Maxillofacial Surgery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Keywords
- AI-generated citations, large language models, ChatGPT, citation integrity, maxillofacial surgery, bibliographic errors, AI literacy, editorial oversight, scholarly publishing
- MeSH terms
- Artificial Intelligence, Humans, Periodicals as Topic, Publishing, Surgery, Oral
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Citation integrity in the age of AI: evaluating the risks of reference hallucination in maxillofacial literature. https://app.cochat.ai/papers/10.1016/j.jcms.2025.08.004
- The paper
- Jain, A., Nimonkar, P., & Jadhav, P. (2025). Citation integrity in the age of AI: evaluating the risks of reference hallucination in maxillofacial literature. Journal of Cranio-Maxillofacial Surgery. https://doi.org/10.1016/j.jcms.2025.08.004
