The field of medical imaging diagnostics is witnessing significant advancements with the integration of artificial intelligence, synthetic data generation, and multimodal learning. Researchers are focusing on developing more accurate, robust, and fair models for disease diagnosis and prognosis. Synthetic data generation is emerging as a promising strategy to address limitations in dataset scale and diversity, enabling the creation of large, demographically balanced datasets. Multimodal learning approaches are being explored to leverage the strengths of different modalities, such as vision and language, to improve model performance and fairness. Noteworthy papers in this area include: Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data, which introduces a text-to-image diffusion model for generating clinically plausible chest radiographs with demographic conditioning. LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR proposes a contrastive learning framework for gadolinium-free cardiomyopathy screening using cine CMR sequences. SWiFT: Soft-Mask Weight Fine-tuning for Bias Mitigation presents a debiasing framework that efficiently improves fairness while preserving discriminative performance. Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Models introduces a multimodal prompt-learning framework that jointly debiases and aligns cross-modal representations. These papers demonstrate the potential of innovative approaches to advance equitable and generalizable medical deep learning under real-world data constraints.