  Despite the high diagnostic accuracy of Magnetic Resonance Imaging (MRI),
using MRI as a Point-of-Care (POC) disease identification tool poses
significant accessibility challenges due to the use of high magnetic field
strength and lengthy acquisition times. We ask a simple question: Can we
dynamically optimise acquired samples, at the patient level, according to an
(automated) downstream decision task, while discounting image reconstruction?
We propose an ML-based framework that learns an active sampling strategy, via
reinforcement learning, at a patient-level to directly infer disease from
undersampled k-space. We validate our approach by inferring Meniscus Tear in
undersampled knee MRI data, where we achieve diagnostic performance comparable
with ML-based diagnosis, using fully sampled k-space data. We analyse
task-specific sampling policies, showcasing the adaptability of our active
sampling approach. The introduced frugal sampling strategies have the potential
to reduce high field strength requirements that in turn strengthen the
viability of MRI-based POC disease identification and associated preliminary
screening tools.
