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
Abstract
Chronic hepatitis B virus (HBV) infection remains the leading global antecedent of hepatocellular carcinoma (HCC), yet the surveillance framework on which early detection depends is constrained at both of its load-bearing points: The modest sensitivity of biannual ultrasonography for early-stage tumours, and the bounded discrimination of risk scores that compress a nonlinear, time-evolving process into a small set of additively weighted covariates. Artificial intelligence (AI) has been advanced as a corrective, but the term is applied loosely and the supporting evidence is drawn from heterogeneous populations and from clinical tasks that are frequently conflated. This narrative review appraises AI across four separable tasks in the HBV-HCC pathway: Risk estimation in chronic hepatitis B, early detection during surveillance, diagnostic characterisation of established lesions, and prognosis after diagnosis. We state what is and is not counted as AI, describe the search strategy and selection principles, and categorise every cited study according to whether its evidence is HBV-specific, mixed-aetiology, hepatitis C virus-derived, drawn from general HCC populations, or confined to recurrence and prognosis. Where machine-learning models built on routine clinical variables have been compared with established scores within a single validation cohort, they have discriminated more accurately. Convolutional networks have improved ultrasonographic lesion detection, serological and cell-free DNA methylation models have been applied to early-stage detection beyond alpha-fetoprotein, and multi-omics integration has been used for molecular subclassification. These findings rest on a literature that is predominantly retrospective and single-centre, in which external validation is the exception, calibration is reported inconsistently, explainability is uneven, and meta-analysts repeatedly judge the underlying studies to be at high risk of bias. Evidence drawn specifically from hepatitis B cohorts remains a minority of the published work, and no study has demonstrated that an AI-guided strategy improves stage at diagnosis or survival. Prospective multicentre validation, transparent calibration, adherence to TRIPOD+AI and PROBAST+AI, and deliberate inclusion of African and South Asian populations are therefore prerequisites rather than refinements.
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.
