Multi-visit magnetic resonance (MR) image reconstruction models utilize information from a previous MR exam to enhance the efficiency of the current exam, thereby speeding up the process while maintaining the image reconstruction quality. One concern with multi-visit reconstruction models is the potential for biases, such as not properly reflecting anatomical changes, that may appear in the reconstructed images when leveraging information from a previous scan. Moreover, the quality of the reconstructed images may be influenced by the time gap between scans. To address these concerns, a series of challenging test scenarios was devised and performed to evaluate the robustness of the multi-visit integration module (MIM). Our analysis shows that despite variations in the quality of the single-visit reconstruction, the multi-visit reconstruction exhibits significant enhancement ( \(p <0.001\) ) by leveraging prior data available from previous scans. Our findings demonstrate that the MIM is robust to the time interval between scans and that synthetic lesions can be used to further enhance its robustness. Our study offers valuable insights for future advancements in the field.