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

Table 7 Roadmap for clinical translation of artificial intelligence in advanced hepatocellular carcinoma

Priority area	Required action	Goal
Validation	Multicenter, temporal, prospective evaluation	Transportability
Causal modeling	Incorporate counterfactual ML/uplift modeling	True treatment guidance
Reporting	Adherence to TRIPOD-AI/CONSORT-AI	Transparency
Calibration	Brier score, calibration plots	Reliable absolute risk
Clinical impact	Decision curve analysis, outcome trials	Demonstrate net benefit
Infrastructure	Federated learning, harmonization protocols	Data diversity and robustness

ML: Machine learning; TRIPOD-AI: Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis-Artificial Intelligence; CONSORT-AI: Consolidated Standards of Reporting Trials-Artificial Intelligence.

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Citation: Meena BL, Behera B, Rudra OS, Sharma D. Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma. Artif Intell Gastroenterol 2026; 7(2): 120311
URL: https://www.wjgnet.com/2644-3236/full/v7/i2/120311.htm
DOI: https://dx.doi.org/10.35712/aig.120311

Meena BL, Behera B, Rudra OS, Sharma D. Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma. Artif Intell Gastroenterol 2026; 7(2): 120311 [DOI: 10.35712/aig.120311]

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