TY - JOUR
T1 - Rethinking Autonomous Surgery
T2 - Focusing on Enhancement over Autonomy
AU - Battaglia, Edoardo
AU - Boehm, Jacob
AU - Zheng, Yi
AU - Jamieson, Andrew R.
AU - Gahan, Jeffrey
AU - Majewicz Fey, Ann
N1 - Funding Information:
Funding/Support and role of the sponsor: This work was supported by the N ational Institutes of Health under award number R01EB030125, and the N ational Science Foundation under award numbers # 1846726 and # 2024839. The sponsors played a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data, and preparation and review of the manuscript. The views in this paper are solely those of the authors and do not necessarily represent the official views of the funding agencies.
Funding Information:
Funding/Support and role of the sponsor: This work was supported by the National Institutes of Health under award number R01EB030125, and the National Science Foundationunder award numbers #1846726 and #2024839. The sponsors played a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data, and preparation and review of the manuscript. The views in this paper are solely those of the authors and do not necessarily represent the official views of the funding agencies.
Publisher Copyright:
© 2021 The Authors
PY - 2021/7
Y1 - 2021/7
N2 - Context: As robot-assisted surgery is increasingly used in surgical care, the engineering research effort towards surgical automation has also increased significantly. Automation promises to enhance surgical outcomes, offload mundane or repetitive tasks, and improve workflow. However, we must ask an important question: should autonomous surgery be our long-term goal? Objective: To provide an overview of the engineering requirements for automating control systems, summarize technical challenges in automated robotic surgery, and review sensing and modeling techniques to capture real-time human behaviors for integration into the robotic control loop for enhanced shared or collaborative control. Evidence acquisition: We performed a nonsystematic search of the English language literature up to March 25, 2021. We included original studies related to automation in robot-assisted laparoscopic surgery and human-centered sensing and modeling. Evidence synthesis: We identified four comprehensive review papers that present techniques for automating portions of surgical tasks. Sixteen studies relate to human-centered sensing technologies and 23 to computer vision and/or advanced artificial intelligence or machine learning methods for skill assessment. Twenty-two studies evaluate or review the role of haptic or adaptive guidance during some learning task, with only a few applied to robotic surgery. Finally, only three studies discuss the role of some form of training in patient outcomes and none evaluated the effects of full or semi-autonomy on patient outcomes. Conclusions: Rather than focusing on autonomy, which eliminates the surgeon from the loop, research centered on more fully understanding the surgeon's behaviors, goals, and limitations could facilitate a superior class of collaborative surgical robots that could be more effective and intelligent than automation alone. Patient summary: We reviewed the literature for studies on automation in surgical robotics and on modeling of human behavior in human-machine interaction. The main application is to enhance the ability of surgical robotic systems to collaborate more effectively and intelligently with human surgeon operators.
AB - Context: As robot-assisted surgery is increasingly used in surgical care, the engineering research effort towards surgical automation has also increased significantly. Automation promises to enhance surgical outcomes, offload mundane or repetitive tasks, and improve workflow. However, we must ask an important question: should autonomous surgery be our long-term goal? Objective: To provide an overview of the engineering requirements for automating control systems, summarize technical challenges in automated robotic surgery, and review sensing and modeling techniques to capture real-time human behaviors for integration into the robotic control loop for enhanced shared or collaborative control. Evidence acquisition: We performed a nonsystematic search of the English language literature up to March 25, 2021. We included original studies related to automation in robot-assisted laparoscopic surgery and human-centered sensing and modeling. Evidence synthesis: We identified four comprehensive review papers that present techniques for automating portions of surgical tasks. Sixteen studies relate to human-centered sensing technologies and 23 to computer vision and/or advanced artificial intelligence or machine learning methods for skill assessment. Twenty-two studies evaluate or review the role of haptic or adaptive guidance during some learning task, with only a few applied to robotic surgery. Finally, only three studies discuss the role of some form of training in patient outcomes and none evaluated the effects of full or semi-autonomy on patient outcomes. Conclusions: Rather than focusing on autonomy, which eliminates the surgeon from the loop, research centered on more fully understanding the surgeon's behaviors, goals, and limitations could facilitate a superior class of collaborative surgical robots that could be more effective and intelligent than automation alone. Patient summary: We reviewed the literature for studies on automation in surgical robotics and on modeling of human behavior in human-machine interaction. The main application is to enhance the ability of surgical robotic systems to collaborate more effectively and intelligently with human surgeon operators.
KW - Automation
KW - Robotic surgery
KW - Surgeon-in-the-loop
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U2 - 10.1016/j.euf.2021.06.009
DO - 10.1016/j.euf.2021.06.009
M3 - Review article
C2 - 34246619
AN - SCOPUS:85110113714
VL - 7
SP - 696
EP - 705
JO - European Urology Focus
JF - European Urology Focus
SN - 2405-4569
IS - 4
ER -