Evidence map›Paper›PMID 34246619›Full record

ReviewEuropean urology focus2021

Rethinking Autonomous Surgery: Focusing on Enhancement over Autonomy.

Edoardo Battaglia, Jacob Boehm, Yi Zheng, Andrew R Jamieson, Jeffrey Gahan, Ann Majewicz Fey

Open access · greenAbstract readReview
In one paragraph

Review in European urology focus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
5.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Observational
  7. Review
  8. Review
  9. Article
  10. Robotic Assistance in Percutaneous Liver Ablation Therapies: A Systematic Review and Meta-Analysis.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024
    Article
  11. Review
  12. Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgery.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2022
    Article
  13. Article
  14. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Edoardo BattagliaDepartment of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA.
Jacob BoehmDepartment of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA.
Yi ZhengDepartment of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA.
Andrew R JamiesonLyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA.
Jeffrey GahanDepartment of Urology, UT Southwestern Medical Center, Dallas, TX, USA.
Ann Majewicz FeyDepartment of Mechanical Engineering, University of Texas at Austin, Austin, TX, USA. Electronic address: ann.majewiczfey@utexas.edu.
The University of Texas at Austin · USSouthwestern Medical Center · US

Funding

A Virtual Coach to Enhance Surgical Training using Human-Centric Modeling and Adaptive Haptic GuidanceR01EB030125 · NIBIB · UNIVERSITY OF TEXAS AT AUSTIN · PI MAJEWICZ FEY, ANN · 2020 to 2023
$1.4M
NIBIB NIH HHS R01 EB030125
6 · The paper itself

Abstract

contextAs 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?

objectiveTo 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.

conclusionsRather 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.

Indexed as

LaparoscopyRoboticsRobotic Surgical ProceduresArtificial IntelligenceHumansMachine LearningAutomationRobotic surgerySurgeon-in-the-loop

Identifiers

PMID34246619
PMCPMC10394949
OpenAlexW3183135617

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.