The widespread adoption of artificial intelligence inclinical cardiology is hampered by a critical factor:the lack of transparency in automated electrocardiogram(ECG) interpretation systems. While deep learning mod-els can accurately classify cardiac abnormalities, their"black-box" nature prevents clinicians from verifying thediagnostic reasoning, undermining clinical trust. To ad-dress this, we developed a multimodal diagnostic frame-work that emulates clinical reasoning by directly linkingECG signal features to their corresponding textual de-scriptions from clinical reports. Our system learns torecognize abnormalities like Left Bundle Branch Block(LBBB) not merely as a classification label, but by identify-ing and associating it with established diagnostic criteria,such as a 'widened QRS complex (> 120 ms)'. Trainedon a large dataset of paired ECG signals and narrativereports, our model achieves diagnostic accuracies compa-rable to conventional supervised models without requiringexplicit training on classification labels. By grounding itspredictions in clinically relevant subfeatures, the systemprovides transparent, verifiable evidence for its conclu-sions. This approach represents a paradigm shift towardAI systems that augment clinical decision-making with in-telligible, evidence-based insights, fostering greater trustand facilitating integration into the diagnostic workflow