Patients' Awareness, Trust, and Acceptance of Robot‐Assisted Dental Implant Surgery: A Cross‐Sectional Survey
Bibliographic record
Abstract
OBJECTIVES: To investigate patients' awareness, trust, and acceptance of robot-assisted dental implant surgery in South China, identify the influencing factors, and evaluate the opportunities and challenges to clinical use. MATERIAL AND METHODS: A cross-sectional survey was conducted from November 2024 to August 2025 in three public hospitals in South China. Electronic questionnaires were distributed to patients who had or were scheduled to undergo implant surgery or were considering dental implant treatment. Participants were assigned to Questionnaire A (patients who underwent robot-assisted implantation) or Questionnaire B (patients without such experience). The questionnaire covered the demographic characteristics, awareness level, trust, acceptance, and experience of those treated with robot assistance. RESULTS: Three hundred and ninety six valid questionnaires were administered. Among these, 26.51% accepted robot-assisted implants, 27.78% rejected, and 45.70% were uncertain. In the inexperienced group (n = 382), 61.00% expressed distrust, which was associated with the age, humanistic care, safety, and emergency capabilities of the new technique. In the experienced group (n = 14), 78.60% of patients expressed their willingness to undergo robotic surgery again. The regression analysis revealed that preoperative information negatively affected satisfaction (β = -0.239, p = 0.019), whereas intraoperative experience exhibited a positive effect (β = 0.268, p = 0.014). CONCLUSIONS: Patients in South China demonstrated limited awareness of robot-assisted dental implant surgery. Trust was mainly influenced by demographics and safety perceptions, whereas satisfaction relied on intraoperative experiences and recovery. The promotion of robot-assisted implant technology should emphasize technical reliability, doctor-patient communication, improved patient experience, and tailored management for different groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".