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Page 59 Xu et al. Art Int Surg 2023;3:48-63 https://dx.doi.org/10.20517/ais.2022.33

Black box phenomenon
With the use of “black-box” algorithms in NNs, even developers do not fully understand the underlying
[90]
mechanisms for automated decision-making , thus making it difficult to explain results to doctors and
patients. In HCC research, programs like DeepDream have been applied to aid NN visualization in tumor
[91]
segmentation of CT liver images . Nonetheless, such post-hoc models have been criticized out of concerns
regarding the fidelity and logicality of explanations provided; Rudin et al. recommend the creation of
inherently explainable models instead . Accepting that AI has already demonstrated greater efficacy in
[92]
recognizing novel patterns and relationships than supervised standard mathematical modeling, the question
remains: is transparency ethically imperative in clinical decision making even if that model far outperforms
any previous modeling? Is this what we should “reasonably hope for” in the future of NN studies in clinical
practice?

Moving towards clinical use
The models developed have shown their potential to add great value to patient care. However, a concerted
effort is required for meta-analyses to sieve out front-runner models and for clinicians to validate those
models both locally and internationally. Secondly, when the models are mature enough, collaboration with
ethics review boards and local government will be crucial for deployment into actual clinical practice. Lastly,
the end-users of the product being clinicians, we should also seek to understand the science behind AI
algorithms, overcome the ‘black-box’ uncertainty of AI, and be confident in using them in practice. As a
community, this is something “we should do”. In order to overcome this, more so in AI-based algorithms
than standard formulae, there is a great necessity for external validation of such models with global
collaborative studies. To this end, the opacity of the AI model requires stringent data entry and quality
assurance that will require careful central control and data monitoring.

CONCLUSION
The utilization of AI in the care of HCC patients is a field that has grown exponentially in the past few
years, with particular areas of care (e.g., liver transplantation and imaging in HCC) being more hotly
debated and investigated than others. We summarize in this article that some AI solutions are also more
acceptable than others - algorithmic approaches may be more easily grasped as compared to NN and DL
models. In addressing the three questions posed by Kant mentioned above, it is clear that AI has established
itself as a tool with limitless learning ability. However, addressing what we should do with this data and
what is reasonable to hope for remains critical to its adoption. Efforts to establish collaborative datasets and
sound external validation in the global scientific and clinical communities will be integral to this. With
sound validation studies in well-curated clinical cohorts and clear reporting standards, some of the five
concerns we put forward are likely to be allayed; thereby, AI application become mainstream in the care of
HCC.

DECLARATIONS
Authors’ contributions
Study concept and design: Bonney GK , Pang NQ
Review of literature: Xu FWX, Tang SS , Soh HN
Drafting of manuscript: Xu FWX , Tang SS , Soh HN
Critical review of manuscript: Xu FWX , Tang SS , Soh HN, Pang NQ, Bonney GK
All authors read and approved the final manuscript: Xu FWX, Tang SS, Soh HN, Pang NQ, Bonney GK

Availability of data and materials
Not applicable.