Applications of Digital Twins in the Healthcare Industry: Case Review of an IoT-Enabled Remote Technology in Dentistry
Bibliographic record
Abstract
Industries are increasing their adoption of digital twins for their unprecedented ability to control physical entities and help manage complex systems by integrating multiple technologies. Recently, the dental industry has seen several technological advancements, but it is uncertain if dental institutions are making an effort to adopt digital twins in their education. In this work, we employ a mixed-method approach to investigate the added value of digital twins for remote learning in the dental industry. We examine the extent of digital twin adoption by dental institutions for remote education, shed light on the concepts and benefits it brings, and provide an application-based roadmap for more extended adoption. We report a review of digital twins in the healthcare industry, followed by identifying use cases and comparing them with use cases in other disciplines. We compare reported benefits, the extent of research, and the level of digital twin adoption by industries. We distill the digital twin characteristics that can add value to the dental industry from the examined digital twin applications in remote learning and other disciplines. Then, inspired by digital twin applications in different fields, we propose a roadmap for digital twins in remote education for dental institutes, consisting of examples of growing complexity. We conclude this paper by identifying the distinctive characteristics of dental digital twins for remote learning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".