MétaCan
Menu
Back to cohort
Record W4414917969 · doi:10.14797/mdcvj.1652

Robotic Systems in Cardiovascular Interventions: Evolving Platforms and the Path Forward

2025· review· en· W4414917969 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMethodist DeBakey Cardiovascular Journal · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFunction (biology)Path (computing)Face (sociological concept)Operator (biology)Emerging technologiesDisruptive technology

Abstract

fetched live from OpenAlex

Interventional radiology is increasingly turning to robotic solutions to overcome limitations of manual catheterization, such as operator fatigue, procedural complications, and radiation exposure. Despite rapid advancements in robotic technologies, existing platforms face barriers to widespread adoption due to complex hardware, nonintuitive controls, and limited modularity, thereby affecting sterility, the absence of true force feedback, heavy reliance on fluoroscopy, high costs, and a lack of truly disruptive innovation. In effect, many systems function more as extensions of the surgeon's hand rather than as disruptive leaps. This review surveys 19 commercial and emerging robotic systems categorized based on the methods and technologies used, with a discussion of benefits and limitations for various specific indications. Integration of imaging, haptics, and economic considerations are also discussed. This comprehensive synthesis aims to offer insights into current capabilities, limitations, and potential future directions for researchers and engineers to advance this domain.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0110.032
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.333
Teacher spread0.289 · 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