Artificial intelligence in the retraction spotlight: trends, causes and consequences of withdrawn AI literature through a systematic bibliometric review

Abstract

Introduction The rapid integration of artificial intelligence (AI) in scientific research has introduced new challenges to academic integrity, with increasing concerns about AI-related article retractions. This study conducts a comprehensive bibliometric analysis of retracted AI-related articles to characterize their prevalence, causes, and impact on scholarly communication. Methods A systematic search was performed in Scopus using the terms “Artificial Intelligence” OR “AI” AND “retract * ” without restrictions on publication year or language. Bibliometric parameters including publication timelines, journal metrics, citation counts, and retraction characteristics were analyzed using VOS Viewer, Bibliometrix, and SPSS. Statistical tests assessed correlations between key variables. Results From an initial yield of 1,152 articles, 335 retracted publications met inclusion criteria after duplicate removal and screening. The analysis revealed that 46.3% (155/335) of retractions occurred in 2023, with a median retraction time of 550 days post-publication. Engineering accounted for 30.4% (102/335) of retractions, while 72.2% (243/335) originated from China. Compromised peer review was the most common retraction reason, though 37.9% (127/335) lacked specific justification. Strikingly, 51.1% (172/335) of retracted articles-maintained field citation ratios >1, indicating persistent scholarly influence. Articles in special issues showed significantly faster submission-to-acceptance timelines ( p = 0.016). Journal editors initiated 98.5% (330/335) of retractions, while author responses revealed disagreement in 35.4% (34/96) of cases where feedback was available. Discussion This study highlights systemic vulnerabilities in AI-related research publication, particularly concerning peer review integrity and prolonged retraction timelines. The continued citation of retracted articles underscores the need for improved retraction alert systems. These findings call for stronger ethical guidelines and technological safeguards to maintain trust in AI-driven scholarly outputs.

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