Citation Inaccuracies in Orthopaedic Surgery: A Novel Classification and Precautions for AI-Generated Bibliographies

Background The integration of artificial intelligence (AI) in medical research is becoming increasingly common. While AI has the potential to assist in various research tasks, its effectiveness in accurately identifying citations and compiling bibliographies is hampered by existing human errors linked to citation inaccuracies. This study aims to: 1) determine the prevalence of citation inaccuracies in orthopaedic surgery literature, and 2) propose a pragmatic classification system to clearly define citation accuracy to determine the validity of AI application in citation collection. Methods We reviewed existing literature on citation accuracy to develop a novel citation accuracy classification system to determine if information is correctly cited. This scoring system distinguishing between major and minor errors, with subcategories that include secondary article citations, oversimplifications, statistical inaccuracies, missing facts, and contradictory conclusions, rated on a scale of 1 to 5. We analyzed 229 references from seven scientific articles and applied our novel classification system to assess accuracy. We also evaluated two artificial intelligence (AI) platforms for their potential to automate citation accuracy checks. Results Improper citations were identified in 27.5% of the 229 references analyzed. Twenty references (8.3%) cited a secondary article. Minor errors were present in 25 references (10.9%), while major errors were found in 21 references (9.2%). Both tested AI platforms failed to complete a thorough review due to an inability to apply the classification system or access full-text sources. Conclusions We present a novel citation accuracy classification system that can be applied across scientific disciplines. Citation inaccuracies are prevalent in orthopaedic surgery literature, with nearly 10% of citations containing major errors. These findings highlight the high prevalence of citation inaccuracies in orthopaedic surgery literature and raise concern about the unchecked use of AI tools that may inadvertently propagate such errors. Combining rigorous human oversight with carefully integrated AI support may help improve the accuracy and reliability of scholarly references in the future. Statement of Clinical Significance The proposed citation accuracy classification system provides a foundation for characterizing inaccuracies, and we emphasize concerns about the unchecked deployment of AI tools that may inadvertently perpetuate the citation errors already present in the literature.