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).

Abstract

ObjectivesTo 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.MethodsOn 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.ResultsIn 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.ConclusionsAI-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.