Nice paper from Google. And a great application of AI agents. Wearables capture a staggering amount of physiological signals every day. CoDa…

Nice paper from Google. And a great application of AI agents. Wearables capture a staggering amount of physiological signals every day. CoDaS is an AI co-data-scientist that turns raw wearable sensor data into clinically relevant biomarkers through an iterative loop of hypothesis generation, statistical analysis, adversarial validation, and literature-grounded reasoning with human oversight. Across 9,279 participant-observations, it surfaced 41 mental-health and 25 metabolic candidate biomarkers, including circadian instability features linked to depression (ρ = 0.252) and a cardiovascular fitness index linked to insulin resistance (ρ = -0.374). Why does it matter? Biomarker discovery is one of the slowest, most expert-bound workflows in medicine. An agentic system that can propose, test, and stress-test candidate biomarkers end-to-end changes the cadence of translational science and starts turning passive consumer sensor data into something clinicians can actually act on. Paper: https://arxiv.org/abs/2604.14615 Learn to build effective AI agents in our academy: https://academy.dair.ai/

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Source: DAIR.AI (X) | 2026-04-18