Abstract In recent years, Artificial Intelligence (AI) has gained increasing traction in medical image analysis. Novel methodologies based on Deep Neural Networks (DNNs) have increasingly enabled the development of solutions that leverage data from multiple sources, including multimodal and synthetic data. Despite notable achievements, current AI systems often struggle with robustness and generalizability, particularly when deployed in real-world clinical scenarios characterized by data heterogeneity, incompleteness, and distribution shifts. This highlights the urgent need for resilient AI, that is, AI capable of maintaining consistent and reliable performance across diverse and imperfect conditions. In this paper, we explore medical image computing from a resilience-oriented perspective, focusing on two key sources of complexity: multimodal and synthetic data. For the former, we address two major challenges: (i) the design of fusion strategies that can effectively handle missing modalities; and (ii) the integration of complementary information to ensure robustness across heterogeneous subject populations. For the latter, we emphasize the critical role of biological plausibility in both the generation and evaluation of synthetic data, proposing physiologically informed pipelines that improve learning in data-scarce settings. We introduce innovative methodologies that advance the state-of-the-art, including a cross-modality calibration mechanism for incomplete acquisitions and a pharmacokinetics-driven synthetic augmentation framework. Our contributions aim to promote robustness, fairness, and generalization, ultimately fostering the development of more resilient AI systems in medical imaging.