Progress of Additive Manufacturing Technology and Its Medical Applications
Bibliographic record
Abstract
Abstract Additive manufacturing (AM), also known as three-dimensional (3D) printing, is a disruptive technology that is revolutionizing many industries. It is gaining considerable attention, particularly in the medical field as it renders the possibilities of building new devices or modifying existing devices to match a patient's anatomy and to produce anatomically exact models, supporting health professionals with diagnostics and surgery preparation. In addition, the free-form building capability of AM allows the designer to have a complete control over the internal architecture of the device, along with tailored mechanical properties, such as compression strength, stiffness, and many surface features. As the processes of AM become well-understood, there is more control over the consistency and quality of the printed parts, positioning this technology for medical applications. With more and more medically approved 3D-printed devices entering the market, the purpose of this paper is to give an overview of the regulatory pathway to the Food and Drug Administration approval of a medical device, along with common AM processes used in the medical industry. To conclude, medical devices that are enabled by AM technology and associated companies will be highlighted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".