Robot ontologies for sensor- and Image-guided surgery
Bibliographic record
Abstract
Robots and robotics are becoming more complex and flexible, due to technological advancement, improved sensing capabilities and machine intelligence. Service robots target a wide range of applications, relying on advanced Human-Robot Interaction. Medical robotics is becoming a leading application area within, and the number of surgical, rehabilitation and hospital assistance robots is rising rapidly. However, the complexity of the medical environment has been a major barrier, preventing a wider use of robotic technology, thus mostly teleoperated, human-in-the-loop control solutions emerged so far. Providing smarter and better medical robots requires a systematic approach in describing and translating human processes for the robots. It is believed that ontologies can bridge human cognitive understanding and robotic reasoning (machine intelligence). Besides, ontologies serve as a tool and method to assess the added value robotic technology brings into the medical environment. The purpose of this paper is to identify relevant ontology research in medical robotic, and to review the state-of-the-art. It focuses on the surgical domain, fundamental terminology and interactions are described for two example applications in neurosurgery and orthopaedics.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".