Enterprise AI Analysis
Artificial intelligence in the retraction spotlight: trends, causes and consequences of withdrawn AI literature through a systematic bibliometric review
The rapid integration of AI in scientific research has led to a significant increase in AI-related article retractions, posing challenges to academic integrity. This study, a comprehensive bibliometric analysis of 335 retracted AI-related articles, reveals systemic vulnerabilities. A striking 46.3% of retractions occurred in 2023, primarily in engineering (30.4%) and with 72.2% of first authors from China. Compromised peer review was the most common cause, yet 37.9% of retractions lacked specific justification. A concerning finding is that 51.1% of retracted articles maintain above-average field citation ratios, indicating their persistent influence. This analysis underscores the urgent need for stronger ethical guidelines, improved peer review, and enhanced retraction alert systems to safeguard trust in AI-driven scholarly outputs.
Key Metrics at a Glance
Understand the scale and implications of retracted AI research in the modern academic landscape.
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Total Retracted AI Articles
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Retractions in 2023
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Median Time to Retraction
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Articles with >1 Field Citation Ratio
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Explore the alarming timelines and patterns of AI-related retractions, highlighting delays in detection and the surge in recent years.
46.3%
of AI retractions occurred in 2023, following peak publications in 2022.
Enterprise Process Flow
Submission to Acceptance (47.5 days)
→
Acceptance to Publication (21 days)
→
Submission to Publication (77.5 days)
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Publication to Retraction (550 days)
| Category | Key Findings |
|---|---|
| Special Issue Articles |
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| Regular Issue Articles |
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Uncover the origins and subject areas most affected by AI-related retractions, revealing regional and field-specific vulnerabilities.
72.2%
of first authors of retracted AI articles originated from China.
Enterprise Process Flow
Engineering Dominance (30.4%)
→
China: 72.2% First Authors
→
Collaboration Clusters (China/India)
→
Q2 Journals (59.7% Retractions)
Journal Impact & Retractions
The Journal of Ambient Intelligence and Humanized Computing showed the highest local impact (H-index of 4) among sources of retracted articles, highlighting potential vulnerabilities even in established outlets. A significant proportion (59.7%) of retractions occurred in Q2 journals, traditionally seen as mid-tier but credible.
Investigate the primary causes of retractions, with a focus on compromised peer review and the role of different stakeholders.
Most Common
retraction reason: Compromised Peer Review.
Enterprise Process Flow
Compromised Peer Review
→
Lack of Specific Justification (37.9%)
→
Journal/Editor Initiated (98.5%)
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Author Disagreement (35.4%)
| Initiator | Key Findings |
|---|---|
| Journal/Editor Initiated |
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| Author Initiated |
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Analyze the lasting influence of retracted AI research, revealing the challenge of problematic literature remaining in scholarly discourse.
51.1%
of retracted articles maintained above-average field citation ratios (>1).
Enterprise Process Flow
Median Citation Count (5)
→
Field Citation Ratio (>1)
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Persistent Scholarly Influence
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Need for Improved Alert Systems
Altmetric Attention Post-Retraction
Retracted articles often garner significant altmetric attention, demonstrating their disproportionate visibility persists post-retraction. A study found that 56.1% of retracted PubMed articles attracted altmetric attention, with 5.5% being highly popular (Altmetric score > 20). This highlights the need for effective retraction alerts beyond traditional citation tracking.
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating AI responsibly.
AI Efficiency & Savings Estimator
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Your Ethical AI Implementation Roadmap
A structured approach to integrating AI responsibly, safeguarding integrity and maximizing long-term value.
Phase 1: Ethical AI Policy Development
Establish clear guidelines for AI tool use, data governance, authorship, and disclosure requirements. Focus on transparency and accountability to mitigate risks like those identified in retracted literature.
Phase 2: Enhanced Peer Review & Oversight
Implement AI-driven screening tools for suspicious content, image irregularities, and citation manipulation. Standardize peer review processes, especially for special issues, to prevent expedited, compromised evaluations.
Phase 3: Training & Cultural Shift
Provide comprehensive training on ethical AI use, its limitations, and potential misuse. Foster a culture that prioritizes rigorous methodology and quality over publication speed, encouraging self-correction and proactive integrity measures.
Phase 4: Advanced Retraction Alert Systems
Collaborate with database providers to improve visibility and linking of retraction status across all platforms. Develop automated alerts for citing retracted work to prevent its persistent influence on scholarly discourse.
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