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  "text": "Nice paper from Google.\n\nAnd a great application of AI agents.\n\nWearables 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.\n\nAcross 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).\n\nWhy does it matter?\n\nBiomarker 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.\n\nPaper: https://t.co/jxoZARoI4G\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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