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McGivern et al. Art Int Surg 2023;3:27-47 https://dx.doi.org/10.20517/ais.2022.39 Page 39
Figure 4. Conceptual mapping of areas of AI research in HPB surgery, stratified by treatment timing. This exercise identified several
areas of overlap (dashed arrows) across different divisions, in addition to several areas where AI would be useful for future research
(purple free text). CT: Computed tomography; IPMN: intraductal papillary mucinous neoplasm; MRI: magnetic resonance imaging;
PHLF: post hepatectomy liver failure; POPF: postoperative pancreatic fistula.
Figure 5. Number of studies utilizing large datasets stratified by AI approach.
of research. This is an example of object detection and is a task well suited to laparoscopic cholecystectomy.
Madani et al. describe a deep learning algorithm that intraoperatively recognizes “go,” or “no-go” areas of
[108]
dissection to minimize the risk of adverse events such as bile duct injury .
