Jingkun Yue

Guangyu Wang, Jingkun Yue, Siqi Zhang et al.

A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics that allow future trajectories to be simulated before actions are taken. We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation. We outline the data infrastructure, evaluation benchmarks, safety constraints and governance frameworks required. Biomedical world models may provide a foundation for simulation-guided, closed-loop and experimentally actionable biomedical discovery.

Jingkun Yue, Siqi Zhang, Zinan Jia et al.

Visual grounding is essential for precise perception and reasoning in multimodal large language models (MLLMs), especially in medical imaging domains. While existing medical visual grounding benchmarks primarily focus on single-image scenarios, real-world clinical applications often involve sequential images, where accurate lesion localization across different modalities and temporal tracking of disease progression (e.g., pre- vs. post-treatment comparison) require fine-grained cross-image semantic alignment and context-aware reasoning. To remedy the underrepresentation of image sequences in existing medical visual grounding benchmarks, we propose MedSG-Bench, the first benchmark tailored for Medical Image Sequences Grounding. It comprises eight VQA-style tasks, formulated into two paradigms of the grounding tasks, including 1) Image Difference Grounding, which focuses on detecting change regions across images, and 2) Image Consistency Grounding, which emphasizes detection of consistent or shared semantics across sequential images. MedSG-Bench covers 76 public datasets, 10 medical imaging modalities, and a wide spectrum of anatomical structures and diseases, totaling 9,630 question-answer pairs. We benchmark both general-purpose MLLMs (e.g., Qwen2.5-VL) and medical-domain specialized MLLMs (e.g., HuatuoGPT-vision), observing that even the advanced models exhibit substantial limitations in medical sequential grounding tasks. To advance this field, we construct MedSG-188K, a large-scale instruction-tuning dataset tailored for sequential visual grounding, and further develop MedSeq-Grounder, an MLLM designed to facilitate future research on fine-grained understanding across medical sequential images. The benchmark, dataset, and model are available at https://huggingface.co/MedSG-Bench

Haijiang Tian, Jingkun Yue, Xiaohong Liu et al.

Medical images are often more difficult to acquire than natural images due to the specialism of the equipment and technology, which leads to less medical image datasets. So it is hard to train a strong pretrained medical vision model. How to make the best of natural pretrained vision model and adapt in medical domain still pends. For image classification, a popular method is linear probe (LP). However, LP only considers the output after feature extraction. Yet, there exists a gap between input medical images and natural pretrained vision model. We introduce visual prompting (VP) to fill in the gap, and analyze the strategies of coupling between LP and VP. We design a joint learning loss function containing categorisation loss and discrepancy loss, which describe the variance of prompted and plain images, naming this joint training strategy MoVL (Mixture of Visual Prompting and Linear Probe). We experiment on 4 medical image classification datasets, with two mainstream architectures, ResNet and CLIP. Results shows that without changing the parameters and architecture of backbone model and with less parameters, there is potential for MoVL to achieve full finetune (FF) accuracy (on four medical datasets, average 90.91% for MoVL and 91.13% for FF). On out of distribution medical dataset, our method(90.33%) can outperform FF (85.15%) with absolute 5.18 % lead.