US Food and Drug Administration Novel Healthcare Devices and Apps: A Microreview
Bibliographic record
Abstract
FigureThe advancing array of biomedical technologies developed by medical device manufacturers include, but are not limited to, the use of microchip technology that readily adapts to computer-driven miniature motors, sensors, actuators, and transducers. In addition, these small technologies comprise parts of medical devices, including “flexible robots” and sensors that are woven into fabrics and smart dressings.1,2 It is now commonplace to couple the driver for these devices into “smart phones” or tablets. The miniaturization of computers allows for “point-of-service” application. Cell phones and mobile (handheld) devices are no longer limited to communication; they now incorporate computational devices specifically designed not only to drive but also to control healthcare applications (devices). These small devices now have the capability of using data collected from cameras, ultrasound, and near-infrared scanners. Specifically, in the wound care realm, there are opportunities to monitor wound healing in real time and even measure treatment outcomes. The Food and Drug Administration (FDA) categorizes the devices under the following framework3: Mobile platforms (handheld), commercial off-the-shelf computing platforms, with or without wireless connectivity. Examples include smart phones, tablet computers, or other portable computers. Mobile application (app) is defined as a software application that can be executed (run) on a mobile platform (ie, commercial off-the-shelf) or a web-based software application that is tailored to a mobile platform but is executed on a server. Mobile medical app: For purposes of this guidance, a “mobile medical app” meets the definition of device in section 201(h) of the Federal Food, Drug, and Cosmetic Act and is intended to be used as an accessory to a regulated medical device or to transform a mobile platform into a regulated medical device. The FDA guidance document for medical mobile apps and indications for regulatory approval was updated in February 2015.3 In general, the FDA regulates those apps that meet the definition of a medical device and that pose a risk to patient safety if they do not function as intended. Medical apps that provide medical information or content are generally not required to seek regulatory approval. However, medical apps that incorporate diagnostics or primary prevention and disease prevention recommendations are generally subject to individualized FDA enforcement discretion. If those devices measure healing and/or capture diagnostic images, they can be subject to disclosure in a medical malpractice case, especially if they cause or document a safety risk. The FDA now encourages input about these devices from the end user as well—the patient.4 The application/approval 510(k) process for mobile device and mobile medical app developers is much shorter than the application/clearance process for medical devices, with most 510(k) submissions cleared within 1 year.5 We are beginning to see more handheld wound care diagnostic devices published in our journal and other peer-reviewed media, including the following examples: FDA-approved “Scout”—an image capture device (WoundVision, LLC, Indianapolis, Indiana) to measure wound length and width and wound perimeter.6 A handheld, Portable Real-time Optical Detection Identification and Guide for Intervention (MolecuLight, Toronto, Ontario, Canada) that enables noncontact, real-time, high-resolution visualization and differentiation of key pathogenic bacteria through their endogenous autofluorescence, as well as connective tissues in wounds.7 An ultraportable near-infrared optical scanner has been developed at the Optical Imaging Laboratory that can perform noncontact 2-dimensional area imaging of the wound site.8 This research was supported by the National Institutes of Health (R15CA119253) and Florida International University Division of Research, Miami, Florida. This month’s continuing education article (page 567) gives us a systematic review and meta-analysis on the efficacy of monitoring devices in support of prevention of pressure injuries.Figure
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.102 | 0.050 |
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".