Exploring Impediments Imposed by the Medical Device Regulation EU 2017/745 on Software as a Medical Device
Bibliographic record
Abstract
In light of rapid technological advancements, the health care sector is undergoing significant transformation with the continuous emergence of novel digital solutions. Consequently, regulatory frameworks must continuously adapt to ensure their main goal to protect patients. In 2017, the new Medical Device Regulation (EU) 2017/745 (MDR) came into force, bringing more complex requirements for development, launch, and postmarket surveillance. However, the updated regulation considerably impacts the manufacturers, especially small- and medium-sized enterprises, and consequently, the accessibility of medical devices in the European Union market, as many manufacturers decide to either discontinue their products, postpone the launch of new innovative solutions, or leave the European Union market in favor of other regions such as the United States. This could lead to reduced health care quality and slower industry innovation efforts. Effective policy calibration and collaborative efforts are essential to mitigate these effects and promote ongoing advancements in health care technologies in the European Union market. This paper is a narrative review with the objective of exploring hindering factors to software as a medical device development, launch, and marketing brought by the new regulation. It exclusively focuses on the factors that engender obstacles. Related regulations, directives, and proposals were discussed for comparison and further analysis.
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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 | 0.040 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".