Exploring the barriers in medical additive manufacturing from an emerging economy
Bibliographic record
Abstract
Sustainable medical additive manufacturing (SMAM) is becoming the next Industry 4.0 technology to revolutionise the medical industry. The adoption of SMAM offers several advantages and brings a paradigm shift in complex manufacturing geometry with improved quality, speed, cost, and sustainability in medical sectors. This research aims to identify the adoption barriers of SMAM in the Indian context. The research design involves two-step procedures: First, a literature review was conducted to determine the barriers to SMAM technology adoption. Later, these were validated by a panel of experts from industry and academia. Second, a hybrid ISM-DEMATEL methodology was deployed to establish and evaluate the cause-effect relationship between the validated barriers. Our findings suggest that among the identified barriers, infrastructural barriers were the most important for adopting SMAM in India, followed by a lack of long-term planning, operational barriers, and supply-demand barriers. Further, it also identified net cause driving barriers, including financial barriers, legal and policy barriers, technological barriers, and management barriers to SMAM adoption. As the adoption of SMAM offers several advantages, including a shift in complex manufacturing geometry with improved quality, speed, cost, and sustainability in medical sectors, these findings assume significance and will help decision-makers overcome complex barriers to SMAM adoption.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".