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Figure 3. Obstacles to the development and translation of AI in surgery based on key elements and stakeholders such as data, clinicians,
researchers (e.g., computer and data scientists), industry, and patients.

If complications of a procedure were to occur as the result of a black box algorithm, it would be increasingly
difficult to describe the reason for these complications to the patient. Such concerns have driven an interest
in explainable AI - methods that seek to impart at least some sense of meaning to the outputs generated by
complex computational processes . However, incorporation of explainability alone will not solve all
[26]
problems associated with the use of black box AI approaches, and rigorous assessment of algorithms
remains key to responsible and ethical use .
[27]
Stakeholder obstacles
Beyond technical obstacles presented by data and methods, behavioral and regulatory incentives play a large
role in both positively and negatively influencing the progress of AI for HPB surgery. Clinicians, particularly
with growing workforce shortages in healthcare, are subject to increasing demands on clinical productivity.
Such pressure can reduce the time and bandwidth to engage in important aspects of professional
development and self-improvement, including academic contributions and continuing medical education.
Furthermore, while establishing a culture of safety has become the focus of most surgical departments, fear
of litigation and unclear hospital policies continue to drive hesitance in systematically engaging in
intraoperative video review . For computer and data scientists, lack of access to the clinical environment
[28]
may limit their ability to translate their research into clinically meaningful applications. In addition, the
academic pressure to produce innovative methods or state-of-the-art results rather than practical
applications may limit the translation of some of these methods within an academic environment. Thus,
collaboration with industry partners may be helpful in getting these technologies from the laboratory to the
clinic and the operating room. However, the need for a significant return on investment as well as business
practices that may limit collaboration with competing companies could limit the availability and
accessibility of these technologies. Finally and most importantly, patients are the key drivers of AI
technologies as patient data is used to develop AI models and the intended beneficiaries of AI models are
patients for whom we hope to improve care. It is, therefore, important to ensure that patient privacy and
autonomy are respected and that patient perspectives are incorporated into the delivery models for AI.

HPB SURGEONS’ ROLE IN ADVANCING AI
While much of the work in developing AI applications in HPB surgery may seem technical, surgeons play a
key role in ensuring that these technologies are translated from the research environment into clinical