  Foundation models are experiencing a surge in popularity. The Segment
Anything model (SAM) asserts an ability to segment a wide spectrum of objects
but required supervised training at unprecedented scale. We compared SAM's
performance (against clinical ground truth) and resources (labeling time,
compute) to a modality-specific, label-free self-supervised learning (SSL)
method on 25 measurements for 100 cardiac ultrasounds. SAM performed poorly and
required significantly more labeling and computing resources, demonstrating
worse efficiency than SSL.
