Factors Shaping Public Perceptions of a Range of Robotic Technologies in Surgery: Cross-Sectional Web-Based Survey
Bibliographic record
Abstract
Background: Within the surgical field, there has been an evolution in the application of robotic technology. Fully automatic robotic systems and augmented visualization tools are being introduced and may eventually replace existing surgical extenders such as the da Vinci surgical system. The literature on public perception of robotic surgery is growing, though specific drivers of these attitudes remain under investigation. Objective: The aim of this study is to investigate the underlying motivators of public perceptions toward robotic surgeries with varying levels of autonomy through a formal technology acceptance model. Methods: An online survey was distributed via the Amazon Mechanical Turk platform. Survey participants were provided definitions of a continuum of robotic technologies: robotic surgical extenders (technology without independent actions), semiautonomous robotic surgery (technology that provides guidance to the surgeon and requires surgeon input), and fully autonomous robotic surgery (technology that performs tasks autonomously without direct human interaction). The survey assessed overall attitudes toward each application of robotic technology in surgery and included questions delineating specific receptivity based on (1) perceived usefulness, (2) social risk, (3) time risk, (4) personal risk, and (5) reliability. A technology acceptance model was built to identify associations between these factors and overall attitudes toward robotic and semiautonomous surgeries. Results: A total of 1221 survey responses were recorded (mean age 38, SD 12 y; females: n=635, 52%). Individuals were more willing to accept robotic surgical extenders and semiautonomous robotic surgery compared to autonomous robotic surgery. Higher levels of education and better self-reported health were correlated with more positive attitudes toward autonomous robotic surgery. Perceptions of these technologies were not associated with age, gender, or income. Overall, attitudes toward robotic technologies in surgery were driven by views on the reliability, safety, and efficiency of the procedures. There was less concern regarding time risk and social risk associated with robotic and semirobotic surgeries. Conclusions: The public is more accepting of semiautonomous surgery and surgical extenders than fully autonomous surgery. General perceptions of the reliability, safety, and efficiency of these technologies drive variations in attitude. Time and social risk do not appear to have a significant impact on receptivity. Understanding these perspectives can help guide education within an advancing surgical field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".