Exploring the Ethical Landscape of Artificial Intelligence in Dentistry: Insights From a Cross-Sectional Study
Bibliographic record
Abstract
Background The emergence of artificial intelligence (AI) in dentistry offers exciting prospects, alongside notable ethical hurdles. As AI capabilities advance, it is essential to comprehend the implications for dental practices, patient well-being, and the dentist-patient connection. This study aims to explore the ethical considerations and challenges associated with the use of AI in dentistry. Methods A web-based survey was conducted among dentists to gain a deeper understanding of their thoughts, experiences, and concerns regarding the implementation of AI in dental practice. The survey explored various aspects such as the ethical implications of AI, its effects on the relationship between dentists and patients, and the significance of human supervision in AI-assisted decision-making. Results The results of the study underscore the intricate ethical considerations that must be taken into account when incorporating AI technology into dental care. Dental professionals conveyed a preference for AI to serve as a supplement to human expertise, rather than a replacement, underscoring the significance of retaining human oversight and direction. Various concerns were raised regarding the potential for AI to influence clinical judgments, the importance of transparency in AI algorithms, and the necessity of safeguarding patient information. Nevertheless, the participants acknowledged the potential benefits of AI in improving diagnostic precision, treatment planning, and administrative efficacy. Conclusion The utilization of AI in dentistry is undoubtedly advantageous, but its implementation must be handled with care and balance. To achieve this, it is crucial to adhere to ethical standards, maintain a continuous commitment to professional education, and prioritize the preservation of the dentist-patient relationship. AI should be viewed as a tool that complements the dentist's expertise, with human judgment remaining paramount in clinical decision-making.
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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 | 0.042 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".