Digitalization in Biomedical Supply Chain:A Systematic Literature Review and Future Directions
Bibliographic record
Abstract
This paper presents the findings of a systematic literature review focused on digital transformation within biomedical supply chains (BMSCs). The review analyzed peer-reviewed articles published to identify digital enablers and barriers, classify applied technologies, and highlight trends across literature. Using a function-oriented framework based on the SCOR-DS model, the study categorizes blockchain, smart contracts, AI/ML, cloud computing, IoT, RFID, digital twin and additive manufacturing as digital technologies according to their contributions across planning, sourcing, manufacturing, delivery, and returns. The findings highlight that while significant attention has been given to technologies supporting traceability, real-time monitoring, and automation, limited studies explore the integration of underrepresented tools such as digital twins and 3D printing. The research identifies critical enablers such as enhanced visibility and process optimization and barriers including cybersecurity risks, high implementation costs, and interoperability issues. The review contributes a structured classification and graphical synthesis of digital technologies in BMSCs, filling a gap in current literature that often overlooks function-based integration. These findings establish a foundation for future research into interdependent digital factors influencing supply chain performance, offering practical insights to support the design of more efficient, resilient, and technology-driven biomedical supply chains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".