Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review

Kieran McGivern, Thomas Drake, Stephen Knight, James Lucocq, Miguel O. Bernabeu, Neil Clark, Cameron Fairfield, Riinu Pius, Catherine A Shaw, Sohan Seth, Ewen M Harrison*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Aim: Artificial Intelligence (AI) and its applications in healthcare are rapidly developing. The healthcare industry generates ever-increasing volumes of data that should be used to improve patient care. This review aims to examine the use of AI and its applications in hepatopancreatic and biliary (HPB) surgery, highlighting studies leveraging large datasets.

Methods: A PRISMA-ScR compliant scoping review using Medline and Google Scholar databases was performed (5th August 2022). Studies focusing on the development and application of AI to HPB surgery were eligible for inclusion. We undertook a conceptual mapping exercise to identify key areas where AI is under active development for use in HPB surgery. We considered studies and concepts in the context of patient pathways - before surgery (including diagnostics), around the time of surgery (supporting interventions) and after surgery (including prognostication).

Results: 98 studies were included. Most studies were performed in China or the USA (n = 45). Liver surgery was the most common area studied (n = 51). Research into AI in HPB surgery has increased rapidly in recent years, with almost two-thirds published since 2019 (61/98). Of these studies, 11 have focused on using “big data” to develop and apply AI models. Nine of these studies came from the USA and nearly all focused on the application of Natural Language Processing. We identified several critical conceptual areas where AI is under active development, including improving preoperative optimization, image guidance and sensor fusion-assisted surgery, surgical planning and simulation, natural language processing of clinical reports for deep phenotyping and prediction, and image-based machine learning.

Conclusion: Applications of AI in HPB surgery primarily focus on image analysis and computer vision to address diagnostic and prognostic uncertainties. Virtual 3D and augmented reality models to support complex HPB interventions are also under active development and likely to be used in surgical planning and education. In addition, natural language processing may be helpful in the annotation and phenotyping of disease, leading to new scientific insights.
Original languageEnglish
Pages (from-to)27-47
JournalArtificial Intelligence Surgery
Issue number1
Publication statusPublished - 27 Mar 2023

Keywords / Materials (for Non-textual outputs)

  • Artificial Intelligence
  • big data
  • surgery
  • liver
  • pancreas
  • biliary


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