Continuous glucose traces look like one noisy curve. They are not. They contain a slow baseline and short-term deviations from meals, activity, and sensor artifacts. GlucoFM is built around that split.

The model is a lightweight, self-supervised foundation model for continuous glucose monitoring. One stream tracks slower metabolic state. The other tracks transient events. Time of day is preserved. Missing readings are masked rather than invented. Pretraining predicts latent structure instead of reconstructing raw noise, and uses augmentations that mimic real device failures: drift, sparse sampling, brief disconnects.

A new AI framework turns routine retinal imaging into a powerful noninvasive window for simultaneous detection of multiple metabolic and endocrine disorders.

The system combines quality-aware modules with frozen foundation models to deliver accurate, interpretable predictions from standard eye scans in under 30 seconds.

A new agentic framework called SPARK uses language as a universal interface to autonomously generate biologically meaningful hypotheses and convert them into analytical tools directly from routine H&E pathology slides.

No hand-crafted features or task-specific retraining are required. The multi-agent system handles the full pipeline: