A review of advancements of artificial intelligence in dentistry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence (AI) has been used in healthcare for decades and has the potential to revolutionize dentistry by solving multiple clinical problems and making the work of clinicians easier. In particular, the study of AI applications in periodontal disease and cariology is important because these are two major areas of concern in dental health. Periodontal disease, which affects the gums and bone surrounding the teeth, is a major cause of tooth loss in adults. Cariology, the study of dental decay, is also an important area of focus for AI research. AI algorithms can be used to analyze dental images and detect early signs of decay that may be missed by human dentists. The review first discusses the history of AI in healthcare and then highlights some of the ways technology has improved dentistry and then describe some basic AI models such as artificial neural networks (ANNs), convolutional neural networks (CNNs), and random forest. The article then delves into how AI is involved in periodontal disease, cariology, endodontics, prosthodontics, and orthodontics including classifying different types of periodontal disease, identifying areas of bone loss, determining the severity of the disease, analyzing dental images, and detecting early signs of diseases. On the other hand, the application of AI in dentistry is relatively uncommon because implementing AI technologies in dentistry presents several challenges that need to be addressed for successful implementation of AI technologies in dentistry.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it