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Review
. 2020 Dec;36(6):450-455.
doi: 10.1159/000511351. Epub 2020 Nov 4.

Artificial Intelligence-Assisted Surgery: Potential and Challenges

Sebastian Bodenstedt  1   2 , Martin Wagner  3 , Beat Peter Müller-Stich  3 , Jürgen Weitz  4   2 , Stefanie Speidel  1   2
Affiliations
Review

Artificial Intelligence-Assisted Surgery: Potential and Challenges

Sebastian Bodenstedt et al. Visc Med. 2020 Dec.

Abstract

Background: Artificial intelligence (AI) has recently achieved considerable success in different domains including medical applications. Although current advances are expected to impact surgery, up until now AI has not been able to leverage its full potential due to several challenges that are specific to that field.

Summary: This review summarizes data-driven methods and technologies needed as a prerequisite for different AI-based assistance functions in the operating room. Potential effects of AI usage in surgery will be highlighted, concluding with ongoing challenges to enabling AI for surgery.

Key messages: AI-assisted surgery will enable data-driven decision-making via decision support systems and cognitive robotic assistance. The use of AI for workflow analysis will help provide appropriate assistance in the right context. The requirements for such assistance must be defined by surgeons in close cooperation with computer scientists and engineers. Once the existing challenges will have been solved, AI assistance has the potential to improve patient care by supporting the surgeon without replacing him or her.

Keywords: Artificial intelligence in surgery; Cognitive surgical robotics; Robot-assisted surgery; Sensor-enhanced operating room; Surgical data science; Workflow analysis.

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1
Potential of artificial intelligence (AI) in surgery based on a sensor-enhanced operating room (SensorOR).
Fig. 2
Fig. 2
Left: example of generating synthetic images based on simple laparoscopic simulations. Middle: generative adversarial networks translate these images so they look like real laparoscopic images. These images, along with their generated labels, can be used without further annotation effort. Right: machine-learning-based detection of the surgical phase and prediction of the duration of the remaining procedure during laparoscopic cholecystectomy.
See this image and copyright information in PMC

References

    1. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019 Jan;25((1)):24–9. - PubMed
    1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May;521((7553)):436–44. - PubMed
    1. Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018 Mar;2((3)):158–64. - PubMed
    1. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb;542((7639)):115–8. - PMC - PubMed
    1. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020 Jan;577((7788)):89–94. - PubMed