AI Publication Citation Impact in Oral and Maxillofacial Radiology Journals, 2017-2024

Journal Citation Reports data from 2017 to 2024 for four oral and maxillofacial radiology journals, manually coded by author Andy Wai Kan Yeung. The dataset quantifies how artificial intelligence publications contribute to journal impact factors. It contains citation counts and item classifications for AI and non-AI articles and reviews.

Use Cases

• Analyze the disproportionate citation impact of AI papers based on the reported 4.3-4.4x higher citation rates per item.
• Model topic-specific citation acceleration in fast-moving research areas based on the described notional JIF calculations.
• Study editorial policy implications for journal impact factor based on the reported contribution of AI papers (10.9%-53.7%) to JIF-accountable citations.
• Compare citation patterns across document types (articles vs. reviews) based on the manual coding described in the methods.

Strengths

• Data spans an 8-year period from 2017 to 2024.
• Manual coding achieved high inter-observer agreement (κ = 0.956).
• Includes descriptive indicators like notional JIF to illustrate AI contribution.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (28.4 KB), indicating limited scope.

Provenance

Source

Journal Citation Reports (JCR) data for four specified journals.

Collection Method

Data retrieved and manually coded by two observers.

Time Range

2017-2024

Freshness

Last updated 2026-05-08 05:29:48; freshness should be verified.

Geography

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