Background Echocardiography after myocardial infarction (MI) provides clinically-useful information through assessment of regional wall motion abnormalities, but interpretation requires expertise and remains subject to intra- and inter-observer variability. Artificial intelligence (AI) has shown promise in the automatic interpretation of echocardiography images, but it is unclear how explainability of the AI model output will change the overall collaborative performance of the human-AI team. Methods A ResNet18-LSTM model with attention was trained to classify normal vs MI on 127 apical four chamber (A4C) and 120 apical two chamber (A2C) echocardiogram videos from the HMC-QU dataset. Gradient-weighted Class Activation Mapping (GradCAM) was used for visual explanations. An end-user study with eight cardiology trainees compared diagnostic performance across three conditions: (a) echo clips alone, (b) echo clips with AI predictions, and (c) echo clips with AI predictions plus GradCAM. explanations. Results The AI models demonstrated strong discriminative performance with AUCs of 0.9429 (A2C) and 0.9250 (A4C). The end user study revealed that AI alone achieved 80.0% accuracy versus 77.0% for clinicians alone. Surprisingly, combining AI with human judgement did not improve performance, and introducing visual explanations reduced accuracy to 72% and specificity from 93.8% to 83.8% (p = 0.046). Conclusion Our findings suggest that while AI models can effectively detect MI on echocardiographic videos, current explainability techniques may misalign with clinical reasoning, potentially impairing diagnostic performance. Future effective integration of AI into echocardiographic workflows will require not only high accuracy but also AI visual explanation strategies that complement clinician expertise.