Ten Times the Compute Mostly Bought More Medical History

A scaling study trained more than one hundred physiological-waveform foundation models from 20 million to 2.1 billion parameters on as much as 36.3 million hours of signals. On eight MIMIC-III clinical prediction tasks, the authors report that a 720-million-parameter model exceeded their comparison foundation models. Their fitted compute-optimal recipe is strikingly data-heavy: a tenfold compute increase scales model size by only 1.2 times but pretraining hours by 8.2 times. The study also found that larger models used waveform exposure more efficiently and that more pretraining could reduce the number of labeled patients needed at a target error. These are linear-probe results and model-based extrapolations rather than clinical deployment evidence, but they frame the resource question clearly: for physiological signals, the next dollar of compute may belong to a longer history before it belongs to a much larger model.