Preface to the 10th Biennial <scp>COAST</scp> Conference: <scp>AI</scp> ‐ and Biomedicine‐Driven Precision Orthodontics and Craniofacial Care
Bibliographic record
Abstract
OBJECTIVE: The Consortium for Orthodontic Advances in Science and Technology (COAST) convened for its 10th biennial conference titled 'AI- and Biomedicine-Driven Precision Orthodontics and Craniofacial Care', to explore how artificial intelligence (AI), emerging technologies and biomedical discovery are transforming the foundations of personalised orthodontic care. SETTING: Academicians, researchers, private practitioners, residents and doctoral students met at the UCLA Lake Arrowhead Lodge from the 6th-9th October 2024 for scientific presentations, workshops and facilitated discussions. Thirty-five invited speakers contributed the latest updates on orthodontic and craniofacial research. The meeting was preceded by the Faculty Development Workshop, AI in Orthodontics: Opportunities and Challenges, supported by the American Association of Orthodontists Foundation's Education Innovation Award. MATERIALS AND METHODS: In addition to an educator's workshop, the scientific program was organised around five themed sessions reflecting the convergence of technology and biology in orthodontic therapies. Topics included artificial intelligence in clinical decision support and care; novel approaches in orthodontic therapeutics; temporomandibular disorders and neural modulation of craniofacial pain; molecular and regenerative mechanisms of craniofacial growth and dental development; and innovations for precision treatment, such as sensor-based assessment, 3D printing and digital workflow optimization. RESULTS: Collectively, the presentations and discussions illustrated how AI and data-driven methodologies are beginning to complement biological and clinical expertise, linking imaging, biomechanics and molecular information toward more predictive and patient-specific care. Active discussion centred on both the promise and limitations of AI, emphasising that meaningful progress requires validation, transparency and the ethical integration of AI. Advances in materials, sensor technologies and multi-omics approaches further demonstrated how precision can be achieved when innovation is guided by scientific rigour and clinical context. CONCLUSIONS: The meeting reaffirmed that the future of orthodontics lies in the thoughtful integration of emerging computational and digital technologies, biomedicine and collaborative science. The collective advances represented in this issue continue COAST's mission to build the foundation for translational biomedicine-driven, ethically grounded, precision orthodontic and craniofacial care.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.258 | 0.120 |
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".