Title: Discrete Diffusion Models with MLLMs for Unified Medical Multimodal Generation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Medical Report Generation, Medical Image Analysis, Multimodal Generation, diffusion models

Abstract: Advances in generative medical models are often constrained by modality-specific scenarios that hinder the integration of complementary evidence, such as imaging, pathology, and clinical notes. This fragmentation limits their development to true foundation models that empower medical AI agents to learn from and predict across the full spectrum of biomedical knowledge. To address the challenges, we propose **MeDiM**, the first medical discrete diffusion model that learns shared distributions across different medical modalities without requiring modality-specific components. MeDiM unifies multiple generative tasks: it flexibly translates between images and text or jointly produces image–report pairs across domains in response to user prompts. It builds on a discrete diffusion framework that unifies vision and language modeling by modeling their shared probabilistic distribution. To empower the diffusion process to support unified and versatile medical generation, we employ a multimodal large language model (MLLM) as the diffusion backbone, leveraging its rich prior knowledge and cross-modal reasoning abilities. Because MLLMs are trained with causal (autoregressive) masking while diffusion denoising benefits from bidirectional context, MeDiM introduces two key designs: 1) _removing the causal attention mask_ to enable a fully bidirectional information flow essential for mutual alignment, and 2) _injecting continuous timestep embeddings_ to make the MLLM aware of the diffusion steps. Extensive experiments validate MeDiM as a unified foundation model capable of high-fidelity medical generation across various domains. It achieves high-quality generation on various tasks, including medical image generation (16.60 FID on MIMIC-CXR; 24.19 FID on PathGen) and report generation (0.2650 METEOR on MIMIC-CXR; 0.2580 METEOR on PathGen). In addition, the jointly generated medical pairs improve downstream performance (+6.43% BLEU-1, +18.57% BLEU-2, +31.58% BLEU-3, and +4.80% METEOR in PathGen), which achieves access to multimodal inputs and generate coherent, clinically grounded multimodal outputs.