Comment by brandonb

We trained a foundation model specifically for wearable data: https://www.empirical.health/blog/wearable-foundation-model-...

The basic idea was to adapt JEPA (Yann LeCun's Joint-Embedding Predictive Architecture) to multivariate time series, in order to learn a latent space of human health from purely unlabeled data. Then, we tested the model using supervised fine tuning and evaluation on on a bunch of downstream tasks, such as predicting a diagnosis of hypertension (~87% accuracy). In theory, this model could be also aligned to the latent space of an LLM--similar to how CLIP aligns a vision model to an LLM.

IMO, this shows that accuracy in consumer health will require specialized models alongside standard LLMs.