For as long as AI has been reading hearts on paper, it has done so in silence — accurate, but unaccountable. A new model called SACNN now brings transparency into that silence, achieving up to 98% accuracy in classifying ECG images while revealing, through built-in visual heatmaps, exactly where its attention falls. Developed as a foundational step rather than a finished clinical instrument, this work asks a quiet but consequential question: what becomes possible when medicine no longer has to choose between trusting an algorithm and understanding one?