For how we live.
A January 2025 draft guidance lays out the documentation the agency expects across an AI device's whole life, including bias checks across demographic groups and postmarket monitoring. It is not yet binding.
A federal rule now requires certified electronic health records to publish a standard set of facts about each predictive algorithm they ship, including how it was validated and whether it was tested for fairness.
Algorithms that read short voice recordings can pick up acoustic signs of some conditions. In the largest and most careful studies the signals are real but narrow, sex-specific, and often fail to generalise.
Green-light pulse sensors raise a real concern about melanin, but independent tests find the skin-tone effect small next to device and movement errors — and the validation studies barely include darker skin.
A Lancet Digital Health scoping review found the field's fairness metrics fragmented and rarely clinically validated. A second review found that most studies don't measure fairness at all.
A study running 5.3 million evaluations through nine large language models found eligibility judgments were largely stable across identity labels — except when a patient vignette mentioned homelessness.
PpgAge predicts chronological age from consumer wearable photoplethysmography and tracks disease. The same optical method has documented accuracy problems across skin tones.