Artificial intelligence-driven risk estimation, early detection, diagnostic characterisation and prognosis in hepatitis b-related hepatocellular carcinoma

doi:10.35712/aig.124278

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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. Nov 8, 2026; 7(3): 124278
Published online Nov 8, 2026. doi: 10.35712/aig.124278

Artificial intelligence-driven risk estimation, early detection, diagnostic characterisation and prognosis in hepatitis b-related hepatocellular carcinoma

Pranjal Kashiv, Manish Ramesh Balwani, Khushboo Saxena, Priyanka Tolani, Amit Pasari, Vivek B Kute

Pranjal Kashiv, Department of Nephrology, All India Institute of Medical Sciences, Nagpur 441108, Mahārāshtra, India

Manish Ramesh Balwani, Amit Pasari, Department of Nephrology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha 442001, Mahārāshtra, India

Khushboo Saxena, Department of Nephrology, Institute of Kidney Diseases and Research Center, Ahmedabad 380016, Gujarāt, India

Priyanka Tolani, Department of Internal Medicine, Jawaharlal Nehru Medical College, Wardha 442001, Mahārāshtra, India

Vivek B Kute, Department of Nephrology and Clinical Transplantation, Institute of Kidney Diseases and Research Center, Dr. HL Trivedi Institute of Transplantation Sciences, Ahmedabad 380016, Gujarāt, India

Author contributions: Kashiv P was responsible for interpretation of evidence, drafting of the manuscript, and coordination of all stages of manuscript preparation and revision; Balwani MR contributed to design of the study; Balwani MR and Kute VB contributed to the conception of the study, supervised the work, and critically revised the manuscript for important intellectual content; Balwani MR provided senior oversight; Kashiv P, Saxena K, Tolani P, and Pasari A contributed to data curation and literature review; Saxena K, Tolani P, and Pasari A critically revised the manuscript; Kute VB contributed to overall project administration. All authors have read and approved the final manuscript.

AI contribution statement: The authors used ChatGPT (OpenAI) solely for English language editing and grammatical refinement during manuscript preparation. The AI tool was not used for literature search, data interpretation, scientific writing, generation of conclusions, or decision-making. All scientific content, interpretation, critical revision, and final approval of the manuscript were performed exclusively by the authors, who accept full responsibility for the accuracy, integrity, and originality of the work.

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

Corresponding author: Vivek B Kute, DM, MD, FASN, FRCP, Department of Nephrology and Clinical Transplantation, Institute of Kidney Diseases and Research Center, Dr. HL Trivedi Institute of Transplantation Sciences, Room No. 125, First Floor, Civil Hospital Campus, Ahmedabad 380016, Gujarāt, India. drvivekkute@rediffmail.com

Received: June 10, 2026
Revised: August 5, 2026
Accepted: September 11, 2026
Published online: November 8, 2026
Processing time: 146 Days and 10.5 Hours

Core Tip

Core Tip: Artificial intelligence is applied to four distinct problems in chronic hepatitis B, namely risk estimation, early detection, diagnostic characterisation and prognosis, and this review appraises each separately, reframing hepatocellular carcinoma risk from a static, population-derived score into a dynamic, individualised probability. The discriminative advantage over legacy instruments is reproducible, yet it rests overwhelmingly on retrospective, single-centre, internally validated work in which hepatitis B-specific evidence is a minority. The field’s maturation will be measured not by further increments in the area under the curve but by the rigour of external validation, the candour of calibration, and the equity of deployment.