MétaCan
Menu
Back to cohort

Robotics In Vivo: A Perspective on Human–Robot Interaction in Surgical Robotics

2019· article· en· W2987013658 on OpenAlexaff
Alaa Eldin Abdelaal, Prateek Mathur, Septimiu E. Salcudean

Bibliographic record

VenueAnnual Review of Control Robotics and Autonomous Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRobotRoboticsRobotic surgeryArtificial intelligenceSurgical robotMedical physicsNeurosurgeryPerspective (graphical)Computer scienceMedicineHuman–computer interactionSurgery

Abstract

fetched live from OpenAlex

This article reviews recent work on surgical robots that have been used or tested in vivo, focusing on aspects related to human–robot interaction. We present the general design requirements that should be considered when developing such robots, including the clinical requirements and the technologies needed to satisfy them. We also discuss the human aspects related to the design of these robots, considering the challenges facing surgeons when using robots in the operating room, and the safety issues of such systems. We then survey recent work in seven different surgical settings: urology and gynecology, orthopedic surgery, cardiac surgery, head and neck surgery, neurosurgery, radiotherapy, and bronchoscopy. We conclude with the open problems and recommendations on how to move forward in this research area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.009
Scholarly communication0.0050.007
Open science0.0020.002
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0060.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.282
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations48
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAnnual Review of Control Robotics and Autonomous SystemsSame topicSoft Robotics and ApplicationsFrench-language works237,207