Proposed Framework for Modifications to Artificial Intelligence/Machine Learning (Ai/Ml)-Based Software as A Medical Device (SAMD)
Bibliographic record
Abstract
The rapid adoption of software as a medical device (SAMD) driven by artificial intelligence and machine learning has brought about a fundamental shift in the medical industry. This shift has the potential to greatly improve clinical outcomes and the quality of care provided to patients. This shift has been responsible for a number of key achievements made in recent times. When seen in this context, the proposed legal framework for revisions to the AI/ML-SAMD appears as an essential response to the malleability of these technologies. To successfully navigate the tough process of modifying AI/ML-SAMD with the assistance of this framework. It does this by taking into consideration the need for rapid regulatory scrutiny and making an attempt to combine the promotion of innovation with the simultaneous preservation of patient safety. In other words, it ensures that patient safety is protected while also encouraging innovation. This abstract provides a summary of the fundamental components of the framework, as well as a discussion of the significance of those components with regard to fostering the development of moral AI/ML-SAMD within the context of the healthcare ecosystem. The healthcare sector is undergoing a change as a direct result of artificial intelligence and machine learning, which are improving patient outcomes, diagnostic accuracy, and treatment options. The research emphasizes the significance of specific AI and ML applications as well as the sector’s embrace of this paradigm-shifting technology. In addition, the regulatory framework that has been presented is an important step towards guaranteeing the safe use of AI and ML in the medical field.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".