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condition, but also those removed due to improvement in their condition. It should be noted that OPOM
allocation does not address the issues in liver distribution, nor the resultant geographic disparity that exists
[66]
between the united network for organ sharing (UNOS) regions and donor service areas (DSAs) . Similarly,
for the Korean random forest ML model, despite its superior outcomes, organ shortage is the main hurdle
for organ transplantation and liver allocation remains a major issue .
[71]
Donor matching
Liver transplantation has traditionally relied on MELD score and (in living donors) volumetric matching
between donor and recipient to achieve an ideal pairing. Beyond simply using AI algorithms to derive a
“better MELD score”, there has been a fundamental shift away from recipient selection and ranking alone to
donor-recipient (D-R) matching models. One of the most widely debated models for D-R matching is an
ANN by Briceno et al. analyzing 64 different variables and their effects on the probability of graft survival
and reduction of graft loss . They found that utilizing their ANN yielded superior results compared to
[72]
current validated scores, including MELD, D-MELD, DRI, P-SOF, SOFT, and BAR .
[72]
However, the use of AI in D-R matching is also not without its limitations. A recent 2021 study by Gujio-
Rubio et al. compared modeling techniques using standard statistical methods (including logistic regression
and naive Baynes) to standard machine learning methods (including multilayer perceptron, random forest,
[73]
gradient boosting and support vector machines) and standard scores (MELD, SOFT and BAR) . Of note,
the study concluded that logistic regression (AUC 0.654) outperformed ML techniques (AUC 0.599-0.644)
and also outperformed standard scores . This adds further uncertainty to the true utility of AI techniques
[73]
in liver transplantation, which will be discussed below.

Transarterial chemoembolization
Transarterial Chemoembolization (TACE) is typically used to treat Stage B HCC following the BCLC
guidelines. Patient selection is key to ensuring that patients suitable for upfront resection do not delay
definitive curative treatment. Several models have been developed based on clinical data and CT or MRI
imaging features. These include the ML and DL models developed by Peng et al. , Morshid et al. , Liu
[75]
[74]
[76]
et al. amongst others - these models have produced fairly satisfactory results, with an AUC of 0.93-0.97
for predicting TACE response.
Radiofrequency ablation
RFA is used to treat both early-stage HCC and unresectable diseases. In selected patients, this treatment
modality aims for curative treatment that confers lower morbidity than traditional liver resection and/or
transplantation would. Liang et al. proposed an ML model in 2014 looking at recurrence after RFA,
attaining an AUC of 0.69. In this study, high-risk patients could be identified and followed up closely after
RFA treatment for surveillance. In 2020, Liu et al. further developed a novel DL-based radiomic strategy to
predict 2-year PFS among 419 patients with very early and early-stage HCC, using CEUS images taken one
week prior to liver resection (n = 205) or RFA (n = 214). Their updated model achieved accurate pre-
treatment predictions of future PFS (C-index 0.741 for liver resection, 0.726 for RFA), potentially serving as
[57]
a future tool for patient selection between the two options . All findings are summarized in Table 3.

CURRENT CHALLENGES IN THE APPLICATION OF AI
In his celebrated thesis ‘The Critique of Pure Reason’, Immanuel Kant asks: “What can we know?” “What
should we do?” “What is reasonable to hope for ?” In the application of AI to clinical practice, this is a
[77]
relevant framing for us to consider its further development and its applicability. The current exponential
development of AI and its accompanying hardware has resulted in landmark scientific discoveries to date,