ArticleFrontiers in research metrics and analytics2026
How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).
Article in Frontiers in research metrics and analytics, 2026. 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To quantify how artificial intelligence (AI) publications contribute to journal impact factor (JIF) in oral and maxillofacial radiology (OMFR) journals and to discuss implications for imaging research, peer review, and clinical translation. Methods: On 25 June 2025, Journal Citation Reports (JCR) data (2017-2024) were retrieved for Dentomaxillofacial Radiology, Oral Radiology, Imaging Science in Dentistry, and the Radiology section of Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology (OOOO). For each JCR year, citable items and JIF-accountable citations were exported and manually coded as AI article, AI review, non-AI article, or non-AI review by two observers (κ = 0.956). A descriptive indicator, named as notional JIF, was computed to illustrate the contribution of AI items. Citation rates were compared descriptively across document types. Results: In JCR 2024, AI papers represented 10.9%-25.2% of citable items among OMFR journals yet contributed 31.3%-53.7% of JIF-accountable citations; radiology AI papers contributed materially to OOOO's JIF despite comprising only 1.5%-2.4% of citable items. AI articles and reviews received 4.3-4.4 × more JIF-accountable citations per item than non-AI counterparts. Notional JIFs exceeded actual JIFs in 2020-2022, reflecting small-denominator effects and topic-specific citation acceleration that diminished as more AI papers were published in subsequent years. Conclusions: AI-related publications are associated with higher per-item citation rates in OMFR journals, consistent with recognized patterns of topic-focused citation concentration in fast-moving research areas. These descriptive findings highlight the importance of methodological transparency, robust validation practices, and balanced editorial policies as AI research continues to expand.
Indexed as
Identifiers
What Socratic 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 Socratic graph.