Blockchain for Trustworthy Artificial Intelligence in Dentistry
Bibliographic record
Abstract
The race for developing and marketing the best inline artificial intelligence (AI) solutions is already in full swing in the dental industry. While regulators are trying to keep up with this fast-paced innovation, end users of these technologies must be on guard to navigate this new landscape safely. Trust is the foundation for this guardrail. Although regulatory approvals can provide some level of trust to an AI solution, users must be empowered with the knowledge of essential vocabulary and semantics to ask the right questions to assess the trustworthiness of the solution. This commentary elaborates on one technology proposed to build trustworthiness in AI solutions: blockchain. Further, we enlist a nonexhaustive list of questions for the users to ask when considering AI solutions in dentistry that may claim to use blockchain technology.Knowledge Transfer Statement:The topic discussed in this commentary could serve as an initial inquiry point that deeply probes into the trustworthiness of an AI solution that a user might consider applying in the field of dentistry.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".