Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps
Bibliographic record
Abstract
Personalized orthopedic devices are increasingly favored for their potential to enhance long-term treatment success. Despite significant advancements across various disciplines, the seamless integration and full automation of personalized orthopedic treatments remain elusive. This paper identifies key interdisciplinary gaps in integrating and automating advanced technologies for personalized orthopedic treatment. It begins by outlining the standard clinical practices in orthopedic treatments and the extent of personalization achievable. The paper then explores recent innovations in artificial intelligence, biomaterials, genomic and proteomic analyses, lab-on-a-chip, medical imaging, image-based biomechanical finite element modeling, biomimicry, 3D printing and bioprinting, and implantable sensors, emphasizing their contributions to personalized treatments. Tentative strategies or solutions are proposed to address the interdisciplinary gaps by utilizing innovative technologies. The key findings highlight the need for the non-invasive quantitative assessment of bone quality, patient-specific biocompatibility, and device designs that address individual biological and mechanical conditions. This comprehensive review underscores the transformative potential of these technologies and the importance of multidisciplinary collaboration to integrate and automate them into a cohesive, intelligent system for personalized orthopedic treatments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".