Automated diagnosis and treatment planning in dentistry
Bibliographic record
Abstract
This study aims to investigate the perceptions and experiences regarding automated diagnosis and treatment planning in dentistry. The field of automated diagnosis and treatment planning is rapidly evolving, leveraging advanced technologies such as artificial intelligence (AI) and machine learning to enhance patient care and outcomes. However, there is a need to understand the perspectives of dental professionals regarding the adoption and implementation of these automated systems. A questionnaire survey was conducted among 100 dentists from various dental practices to gather data on their familiarity, usage, and perceptions of automated diagnosis and treatment planning. The survey also explored the perceived benefits, challenges, and future implications of automated systems in dental care. Preliminary findings indicate that the majority of dentists in the sample (80%) have some level of familiarity with automated diagnosis and treatment planning. However, only 45% reported actively using such systems in their practice. Among the dentists using automated systems, the most commonly cited benefits include time-saving (60%), enhanced accuracy (55%), and improved treatment planning (50%). Challenges associated with the adoption of automated systems were also identified.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".