Transparency is a Misplaced Regulatory Focus for Holding Adaptive Software as Medical Devices (SaMDs) Accountable
Quy Mai Adaptive Software as Medical Devices (SaMDs) play an increasingly critical role within clinical settings, assisting physicians with illness detection, diagnosis, and analysis. Use of Artificial Intelligence/Machine Learning (AI/ML) techniques, such as deep machine learning and neural networks, lends adaptive SaMDs unparalleled analytical power, but not without risks. Adaptive SaMDs are typically “black-box,” meaning that they compute data such that no one can determine how it rendered outputs. “Transparency,” in the form of explainability, is frequently raised in policy discussions as a solution to track when the SaMD has erred in computing outputs. The FDA, in seeking to uphold...
Get Out of My Head: An Examination of Potential Brain-Computer Interface Data Privacy Concerns
Kevin Y. Li Brain-computer interfaces (“BCI”), which interpret brain impulses and translate them into real world outputs, currently exist in a variety of forms. With the continued development of BCIs and their increasing complexity, privacy issues will arise in regards to the data that they collect. Existing federal statutes, such as HIPAA, as well as state data privacy statutes offer some protection to BCI users, but it remains to be seen whether these laws will be sufficient to accommodate the amount and sensitivity of the data likely to be generated by future BCIs. Lastly, this article explores the possibility of...