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Xu et al. Art Int Surg 2023;3:48-63 Artificial
DOI: 10.20517/ais.2022.33
Intelligence Surgery

Review Open Access

Augmenting care in hepatocellular carcinoma with
artificial intelligence

2
1,#
3
1,#
Flora Wen Xin Xu , Sarah S Tang , Hann Natalie Soh , Ning Qi Pang , Glenn Kunnath Bonney 3
1
Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore.
2
Department of Internal Medicine, Singapore General Hospital, Singapore 119077, Singapore.
3
Department of Hepatopancreaticobiliary Surgery and Liver Transplantation, National University Hospital, Singapore 119077,
Singapore.
#Authors contributed equally.
Correspondence to: Prof. Glenn Kunnath Bonney, Department of Hepatopancreaticobiliary Surgery and Liver Transplantation,
National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119077, Singapore. E-mail: glenn_bonney@nuhs.edu.sg
How to cite this article: Xu FWX, Tang SS, Soh HN, Pang NQ, Bonney GK. Augmenting care in hepatocellular carcinoma with
artificial intelligence. Art Int Surg 2023;3:48-63. https://dx.doi.org/10.20517/ais.2022.33
Received: 1 Nov 2022 First Decision: 31 Jan 2023 Revised: 22 Feb 2023 Accepted: 16 Mar 2023 Published: 29 Mar 2023

Academic Editors: Derek O’Reilly, Andrew A. Gumbs Copy Editors: Ke-Cui Yang, Yanbing Bai Production Editors: Ke-Cui Yang,
Yanbing Bai

Abstract
Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide and prognosis
remains poor. The recent paradigm shifts in management algorithms of such patients have resulted in unique
challenges in the early identification of HCC, prognosis, surgical outcomes, prioritization of potential transplant
recipients, donor-recipient matching, and so on. In recent years, advancements in artificial intelligence (AI)
capabilities have shown potential in HCC treatment.

In this narrative review, we outline first the different types of AI models that are applied in clinical practice and then
focus on the frontiers of AI research in the diagnosis, prognostication, and treatment of HCC, particularly in
classification of indeterminate liver lesions, tumor staging, survival prediction, improving equity in transplant
recipient selection, prediction of treatment response and prognosis. We show that US coupled with AI-driven
predictive models can provide accurate noninvasive screening tools for early disease. While AI models applied to
contrast-enhanced CT, MRI and PET studies may appear to have limited clinical utility in disease diagnosis and
differentials, owing to their accuracy, we highlighted the importance of such models in predicting pathological
findings preoperatively. Despite the availability of many accurate, sensitive, and specific AI algorithms that
outperform traditional scoring systems, they have not been widely used in clinical practice. The challenges in AI
application, including distributional shift and imbalanced data, lack of standardization, and the ‘black box’

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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