Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma

doi:10.35712/aig.120311

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Artificial Intelligence in Gastroenterology

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2644-3236

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 120311
Published online Aug 8, 2026. doi: 10.35712/aig.120311

Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma

Babu Lal Meena, Bijaylaxmi Behera, Omkar S Rudra, Deepti Sharma

Babu Lal Meena, Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India

Bijaylaxmi Behera, Department of Pediatrics, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India

Omkar S Rudra, Department of Hepatology, Institute of Liver and Biliary Sciences, Vasant Kunj, New Delhi 110070, India

Deepti Sharma, Department of Radiation Oncology, Institute of Liver and Biliary Sciences, Vasant Kuunj, New Delhi 110070, India

Author contributions: Meena BL conceptualized the manuscript, designed the study framework, and prepared the initial draft; Behera B, Rudra OS, and Sharma D contributed to the literature review and critically revised the manuscript for important intellectual content. All authors approved the final version of the manuscript.

AI contribution statement: The authors were solely responsible for the conception, literature review, interpretation, and critical analysis of the manuscript. All scientific content and conclusions were developed and validated by the authors. AI tools (including ChatGPT and Grammarly) were used only for language refinement, structuring of text, and improvement of clarity. No part of the manuscript was generated independently by AI without author oversight and revision. AI tools were not involved in study design, data analysis, interpretation of results, or generation of scientific content. No images or figures were generated using AI; all figures were created by the authors using Microsoft PowerPoint and are provided in editable format.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

Corresponding author: Babu Lal Meena, DM, MD, Department of Hepatology, Postgraduate Institute of Medical Education and Research, Sector 12, Chandigarh 160012, India. drbabupgi@gmail.com

Received: February 24, 2026
Revised: April 19, 2026
Accepted: May 22, 2026
Published online: August 8, 2026
Processing time: 163 Days and 15.9 Hours

Core Tip

Core Tip: Artificial intelligence models in advanced hepatocellular carcinoma frequently demonstrate improved discrimination compared with conventional staging systems; however, most remain retrospective, prognostic rather than predictive, and inconsistently validated across institutions and therapeutic eras. This review critically distinguishes statistical performance gains from clinically meaningful advancement, emphasizing the need for causal modeling, competing-risk adaptation, calibration assessment, and prospective decision-impact evaluation before routine integration into multidisciplinary hepatology practice.