Regulatory Prospective on Software as a Medical Device
Bibliographic record
Abstract
Software is becoming increasingly important in medical devices and digital adoption more broadly. It is becoming more important as a medical device in its own right. (1) Currently the use of software in medical market is growing exponentially and many countries have already set guidelines for quality control and clinical evaluation for SaMD. Millions of users use AI based medical device for the diagnosis & Management of diseases. Regulation for the SaMD, IMDRF published guidance document in 2013, in EU they are regulated by EMA, in Australia they are regulated by TGA and in Canada they are regulated by Health Canada. Regulations of these countries and IMDRF were reviewed and articles of challenges in artificial intelligence based medical devices reviewed. There are also many challenges like cybersecurity, safety, and decommissioning, high cost of device and also the design and development process. The objective is to focus on SaMD’s regulations and Challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".