Bibliographic record
Abstract
A robotic machine is an artificial intelligence machine that can perform semi-autonomous or fully autonomous work. It has basic characteristics such as perception, decision-making and execution, and can assist or even replace humans in completing dangerous, heavy, and complex tasks, improving work efficiency and quality, serving human life, expanding or extending the scope and capabilities of human activities. With the development of digital design and numerical control manufacturing, robot technology has rapidly expanded to all walks of life closely related to human life. From outer space, deep sea, industrial manufacturing to tiny molecules, robots are everywhere. A new generation of robots promises to create even more possibilities in home, workplace and community safety, supporting services, entertainment, education, healthcare, manufacturing and assistance. With the development of digital design and numerical control manufacturing, robot technology has rapidly expanded to all walks of life closely related to human life. From outer space, deep sea, industrial manufacturing to tiny molecules, robots are everywhere. The robot has the characteristics of precision, high efficiency and stability, and has been applied in all walks of life. At present, the application in treatment has achieved phased results. It not only reduces the work intensity of the clinician, but also improves the accuracy of the treatment. This paper reviews the application and research status of robot in medical field.Aic is an artificial intelligence machine which can be used to perform semi-autonomous or fully autonomous work. It has the most basic characteristics .For example perception, decision-making and execution, and can assist or even replace human beings to complete dangerous, heavy and complex work, improve work efficiency and quality, serve human life, expand or extend the scope of human activities and capabilities. This paper reviews the contribution of robots in the field of medicine. The main point of this paper is the help and development of robots to the medical industry.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".