PHENO-RAG: An artificial intelligence tool for guideline-informed management decisions in hepatocellular carcinoma

Clinical decisions in the management of hepatocellular carcinoma are complex and multiparametric, requiring resource-intensive multidisciplinary care and creating challenges for optimal treatment allocation across different healthcare settings. We developed PHENO-RAG, a large language model-based framework that combines patient phenotyping through automated clinical information extraction from real-world clinical notes with treatment decision support, based on international guidelines. Our framework demonstrated concordance of 86.5% with real-world clinical decisions for treatment allocation and 88.6% for clinical complexity assessment, suggesting potential to enhance decision consistency and quality of care. In clinical practice, this AI-assisted framework could help standardize hepatocellular carcinoma management workflows, support training of hepatology and oncology fellows, assist in quality assurance programs, and facilitate more systematic identification of complex cases requiring multidisciplinary consultation, particularly in resource-constrained settings.

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Publication Details

Journal
JHEP Reports
Published
2025-12-20
DOI
https://doi.org/10.1016/j.jhepr.2025.101715
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
Type
article
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article

PHENO-RAG: An artificial intelligence tool for guideline-informed management decisions in hepatocellular carcinoma

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JHEP Reports
Hepatocellular Carcinoma Treatment and Prognosis
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PHENO-RAG: An artificial intelligence tool for guideline-informed management decisions in hepatocellular carcinoma

Roberto Cannella, Raquel Carvalho Montenegro, Giansalvo Cirrincione, Calogero Cammà, Simone Kresevic, Alessandro Grova, G. Cusimano, Ciro Celsa, Giuseppe Cabibbo, Duilio Pagano, Claudia La Mantia, Roberto Miraglia, U Palazzo, Salvatore Gruttadauria, M. Salvato, Alba Sparacino, Sofia Rao, Luigi Maruzzelli, A. Quartararo, V.R. Gaudioso, Mauro Giuffrè, Massimo Midiri, Gabriele Di Maria, Miloš Ajčević, Roberta Ciccia, F. Mercurio, Gaetano Giusino, Valeria Genovese
article en

Abstract

Clinical decisions in the management of hepatocellular carcinoma are complex and multiparametric, requiring resource-intensive multidisciplinary care and creating challenges for optimal treatment allocation across different healthcare settings. We developed PHENO-RAG, a large language model-based framework that combines patient phenotyping through automated clinical information extraction from real-world clinical notes with treatment decision support, based on international guidelines. Our framework demonstrated concordance of 86.5% with real-world clinical decisions for treatment allocation and 88.6% for clinical complexity assessment, suggesting potential to enhance decision consistency and quality of care. In clinical practice, this AI-assisted framework could help standardize hepatocellular carcinoma management workflows, support training of hepatology and oncology fellows, assist in quality assurance programs, and facilitate more systematic identification of complex cases requiring multidisciplinary consultation, particularly in resource-constrained settings.

JHEP ReportsVol. 8(4)
University of Trieste (IT), Department of Health (ES), Université de Picardie Jules Verne (FR), Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione (IT), University of Palermo (IT)
Openalex Percentile: Top 45%
Hepatocellular Carcinoma Treatment and Prognosis
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