ENTERPRISE AI ANALYSIS
Correction: Al-bakri et al. A Hybrid Explainable AI Framework (HXAI) for Accurate and Interpretable Diagnosis of Alzheimer's Disease. Diagnostics 2025, 15, 3118
This analysis reviews a critical correction in academic publication, highlighting the importance of robust post-publication integrity mechanisms, a key area for AI-driven solutions in enterprise research and publishing.
Executive Impact: Ensuring Data Integrity
Even minor corrections carry significant implications for the credibility and reproducibility of research. AI solutions can proactively minimize errors and streamline correction processes.
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Accuracy of Record
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Transparency Enhanced
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Summary of the Correction
The original publication included 'The Alzheimer's Disease Neuroimaging Initiative' as an author. This correction rectifies that, removing it from the author list. The authors affirm that the scientific conclusions presented in the original article are entirely unaffected by this administrative correction. This action ensures the academic record accurately reflects all contributing individuals and entities.
1 Author
Entity Removed from Author List
This change corrects the formal authorship record without altering the scientific findings or conclusions of the research paper.
Updated Author Contributions
Author Contributions
Conceptualization, F.H.A.-b.; Methodology, F.H.A.-b. and W.M.Y.W.B.; Validation, W.M.Y.W.B., F.H.A.-b., R.R.R.I., and H.M.K.; Formal analysis, F.H.A.-b.; Investigation, F.H.A.-b., W.M.Y.W.B., M.S.M., N.M.Y., N.F.A.Y., M.H.F.M.F., A.S.M.J., N.A.M., N.Y., K.B., T.P.M., A.B., Z.Z., D.W., and U.K.A.; Writing—original draft preparation, F.H.A.-b.; Writing—review and editing, all authors; Visualization, F.H.A.-b.; Supervision, W.M.Y.W.B.; Guidance on ensemble learning and XAI, M.N.A.-A.; Medical validation and clinical review, H.M.K. All authors have read and agreed to the published version of the manuscript.
Updated Acknowledgements
Acknowledgements
The authors would like to thank the Optimas Research Group, Center for Advanced Computing Technology (C-ACT), Fakulti Kecerdasan Buatan dan Keselamatan Siber (FAIX), Fakulti Teknologi Maklumat dan Komunikasi (FTMK), and Centre for Research and Innovation Management (CRIM), Universiti Teknikal Malaysia Melaka (UTeM), for providing the facilities and support for this research. The authors also thank the Research Management Center (RMC), Multimedia University, for their valuable support in this research. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI investigators contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report.
Estimate the Impact of AI-Driven Scholarly Integrity
Calculate potential improvements in data governance and publication accuracy through robust AI-powered pre-publication checks and post-publication correction tracking.
Estimated Annual Cost Savings
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Estimated Annual Hours Reclaimed
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Roadmap to Enhanced Publication Governance with AI
A strategic overview of how AI can streamline and secure your publication workflows, from initial submission to post-publication integrity checks.
Phase 1: AI-Powered Integrity Scan Deployment
Integrate AI models for automated pre-submission checks for authorship, data integrity, and ethical compliance. Establish a baseline for accuracy.
Phase 2: Automated Correction Workflow Integration
Implement systems that automatically flag and process necessary corrections, including author list modifications and content updates, ensuring rapid response.
Phase 3: Continuous Monitoring & Audit Trail
Deploy AI for continuous monitoring of published content, detecting potential discrepancies or new information requiring corrections, and maintaining an immutable audit trail for all changes.
Phase 4: Stakeholder Training & Adoption
Provide comprehensive training to editorial teams, authors, and administrators on using AI tools for better publication governance and correction management, fostering a culture of accuracy.
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