BRIDGING REGULATORY GAPS IN DIRECT-TO-CONSUMER HEALTH MONITORING TECHNOLOGIES FOR OLDER ADULTS
Bibliographic record
Abstract
Abstract This presentation explores unique ethical concerns that result from regulatory gaps in direct-to-consumer (DTC) AI health monitoring platforms, with a focus on the current landscape in the United States. Many older adults have multiple co-morbidities and are increasingly encouraged or even expected to continuously monitor their health, including by AI-powered predictive digital technologies. Some medically oriented AI-based algorithms can seek and receive regulatory approval as a medical device. Nonetheless, a much larger category of DTC AI health platforms includes wellness technologies that are not subjected to regulatory scrutiny or approval, even when many unregulated devices collect similar types of data as medical devices and vast amounts of health-related and personal information, blurring the line between medical and commercial data and products. Older adults are generally more risk averse and have limited digital, medical, and statistical literacy, and may also experience cognitive impairments. Enthusiasts often cite the potential of DTC health monitoring technologies to democratize health information and users’ well-being. However, the lack of governance for DTC applications raises unique and intersecting ethical concerns for the field of gerontology, particular around barriers for informed consent, lack of validation for product safety and clinical value, perpetuation of infantilization, non-transparent practices of data sharing, and potential impact of these technologies on familial care relationships and formal health care delivery. This presentation concludes with recommendations for regulatory considerations to uphold monitored older adults’ agency and well-being.
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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 | 0.136 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".