Review of 2013-2014 Mobile Medical Applications: Regulatory Challenges and Precedents
Bibliographic record
Abstract
This project aims to offer a review of mobile medical apps and the most important changes that happened in this sector in 2013 and 2014. We start with a discussion about the regulation of these devices in the United States, the European Union, and Brazil. 2013 was a significant year because of the publication of important guidelines about regulation and classification of mobile apps as medical devices. We further discuss how the manufacturers look at the requirements of safety and efficiency and how they are assessing these outcomes. Next, we explore the value proposition of mobile medical applications. Finally, we explore three case studies of mobile medical applications; one intended for imaging diagnostics, called ResolutionMDTM, by Calgary Scientific Inc., another, MyVisonTrackTM, by Virtual Art and Science, Inc., and a third, unregulated application from GN ReSound called ReSound SmartTM. The information for this project was mainly gathered using secondary research. For this purpose, we used databases, medical, business and mobile health reports, medical journals, and medical and mobile health websites. The time frame for data was mainly between 2010 and 2014, with only one reference each from 2002, 2004, and 2008. As the world becomes increasingly more mobile and moves to a value-based healthcare system, mobile medical applications offer a huge opportunity to provide cost-effectiveness, patient empowerment and good health outcomes. 2013 and 2014 were significant years for the first steps of the regulatory agencies towards more structured guidance on this matter, but health application developers seemingly have yet to fully understand the importance of providing safer and more efficient apps to patients and providers.
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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".