A new preprint demonstrates how a 3D U-Net can suppress artifacts from simultaneous multi-slice spiral bSSFP imaging, slashing both acquisition and reconstruction times dramatically on standard 1.5 T systems. The method acquired two slices simultaneously in free-breathing volunteers, delivering whole short-axis coverage in 15 seconds versus over three minutes for breath-hold references. Reconstruction finished in 30 seconds—fifty times faster than compressed-sensing baselines—while yielding superior image quality scores. Functional measurements showed acceptable agreement with breath-hold cine, with biases under 10 ml for most ventricular volumes. However, the study enrolled only ten healthy volunteers, limiting insight into performance amid arrhythmias, obesity, or post-surgical anatomy where motion and off-resonance effects intensify. Earlier SMS work relied on lengthy iterative solvers that blocked online use; this deep-learning step removes that barrier but inherits the narrow demographic scope common in early cardiac AI papers. Related efforts, such as those exploring radial trajectories with learned priors, suggest broader robustness may require patient-specific fine-tuning. Until tested in mixed cohorts, the claimed leap in accessibility remains promising yet provisional.