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 multi-center study of 22,328 children with cancer treated between 2005 and 2019 at three major Indian centers (AIIMS and RGCI in Delhi, WIA in Chennai) measured the real geographic cost of accessing specialized care.

The median motorized travel time was 161 minutes. Nearly three-quarters of patients (73.5%) traveled more than 60 minutes to reach treatment. Male children and those with solid tumors were more likely to face prolonged travel.

Wearable devices generate enormous amounts of multimodal sensor data (heart rate, motion, skin conductance, temperature, etc.), but most AI models for health are narrow, task-specific, and require large amounts of labeled data. This limits scalability and generalization in real-world settings where data is often fragmented or incomplete.

Google researchers introduce SensorFM, a large sensor foundation model pre-trained self-supervised on over one trillion minutes of wearable data from more than five million consented participants across diverse devices and geographies. It learns a general-purpose representation of human physiology using a missingness-aware framework (AIM) that treats real-world data gaps as meaningful signals rather than noise.

The idea that healthcare simply needs its own UPI moment is seductive but fundamentally flawed. It trivializes one of the most complex information ecosystems ever built.

UPI succeeded because payments rest on a simple universal primitive: identity, authorization, amount, and settlement. A rupee is a rupee. The semantics are finite, deterministic, and often reversible.