Publication Name	World Journal of Hepatology
Manuscript ID	109494
Country	Türkiye

Received	2025-05-13 04:29
Peer-Review Started	2025-05-21 09:12
To Make the First Decision
Return for Revision	2025-06-06 03:32
Revised	2025-06-13 19:56
Second Decision	2025-10-10 02:36
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-10-10 09:48
Articles in Press	2025-10-10 09:48
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-11-18 00:56
Publish the Manuscript Online	2025-11-27 07:53

ISSN	1948-5182 (online)
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Category	Transplantation
Manuscript Type	Review
Article Title	Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications
Manuscript Source	Invited Manuscript
All Author List	Sami Akbulut and Cemil Colak
Funding Agency and Grant Number
Corresponding Author	Sami Akbulut, FACS, MD, Professor, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com
Key Words	Hepatocellular carcinoma; Artificial intelligence; Explainable artificial intelligence; Ensemble learning; Explainable ensemble learning
Core Tip	Explainable artificial intelligence (XAI) seeks to improve the interpretability and transparency of machine learning models in healthcare settings. In this context, Explainable Ensemble Learning, a fundamental strategy within XAI, integrates multiple models, including Random Forest, Extreme Gradient Boosting, and Stacking, to improve classification performance in hepatocellular carcinoma (HCC). Despite their high predictive accuracy, the inherent "black-box" feature of ensemble methods remains a barrier to clinical practice. XAI techniques—such as SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and Gradient-weighted Class Activation Mapping—clarify model predictions, fostering medical trust and interpretability. By combining clinical, genetic, and imaging data with XAI frameworks, diagnosis, staging, and prognosis of HCC can be improved, ultimately supporting transparent and reliable decision-making in healthcare. Future research should focus on model interpretability, data integration, and user-friendly clinical interfaces.
Publish Date	2025-11-27 07:53
Citation	<p>Akbulut S, Colak C. Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications. <i>World J Hepatol</i> 2025; 17(11): 109494</p>
URL	https://www.wjgnet.com/1948-5182/full/v17/i11/109494.htm
DOI	https://dx.doi.org/10.4254/wjh.v17.i11.109494

Full Article (PDF)	WJH-17-109494-with-cover.pdf
Manuscript File	109494_Auto_Edited_064019.docx
Answering Reviewers	109494-answering-reviewers.pdf
Audio Core Tip	109494-audio.m4a
Conflict-of-Interest Disclosure Form	109494-conflict-of-interest-statement.pdf
Copyright License Agreement	109494-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	109494-non-native-speakers.pdf
Peer-review Report	109494-peer-reviews.pdf
Scientific Misconduct Check	109494-scientific-misconduct-check.png
Scientific Editor Work List	109494-scientific-editor-work-list.pdf
CrossCheck Report	109494-crosscheck-report.pdf