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Boutros et al. Art Int Surg 2022;2:213-23 https://dx.doi.org/10.20517/ais.2022.32 Page 221

practice. Perhaps the most straightforward role that surgeons can play is in the collection and annotation of
intraoperative data. As previously discussed, AI algorithms often require large amounts of data to optimize
their performance. For computer vision applications, this data comes primarily in the form of operative
video. Surgeons should seek to clarify institutional rules and policies surrounding the recording, storage,
and use of surgical video. Increasing the pool of available data for training AI algorithms could help to
improve the generalizability of these algorithms and address some elements of representativeness bias in
existing public datasets.

In addition to collecting operative data, surgeons of all training levels are needed to help label or annotate
the data. As previously discussed, supervised and semi-supervised learning approaches are dependent on
having at least some (if not copious amounts of) labeled data from which algorithms can learn. A growing
literature of annotation guides and surgical process ontologies has made the annotation process more
accessible and somewhat more standardized [22,29] . Furthermore, surgical societies such as the Society of
American Gastrointestinal and Endoscopic Surgeons, the Japanese Society for Endoscopic Surgery, and the
European Association of Endoscopic Surgery have been engaging in work that trains their members to
annotate videos for various types of AI projects.

Perhaps the most important contribution that HPB surgeons can make to the field of AI is to participate in
multidisciplinary teams and to educate themselves further about the realities of AI. Computer and data
scientists can work wonders to draw insights from data; however, it is important to have clinicians on the
team who can place such insights into context. The translation of advances in mathematics, statistics, and
computer science to clinical medicine can be difficult, as not all clinical problems fit neatly into a given
mathematical approach or set of tools. Clinical expertise can help to identify missing data, inappropriately
labeled data, or misattributed predictions. It is, thus, important for clinicians to invest time into
understanding key methodological considerations in AI and modeling research to ensure that the literature
[30]
is rigorously evaluated and interpreted before being applied to patients .
CONCLUSION
Artificial intelligence continues to grow at a rapid pace and its applications to surgery are becoming
increasingly appreciated. However, obstacles remain in the further development of clinically applicable,
reliable, and verifiable algorithms that can translate to patient care. While we have provided a brief and
broad overview of some of the terms, techniques, and applications of AI for HPB surgery, it is important for
HPB surgeons to dive more deeply into these topics, critically appraise the literature on AI applications, and
partner with computer and data scientists to further advance the field.

DECLARATIONS
Authors’ contributions
Study conception, drafting, and revision: Boutros C, Singh V, Hashimoto DA
Study conception, review, and revision: Ocuin L, Marks J

Availability of data and materials
Not applicable.

Financial support and sponsorship
None.