Wei-Hung Weng

Abstract Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge.

Abstract Real-world clinical practice is inherently multimodal, relying on the synthesis of patient history with visual information such as medical imagery and clinical documents. Although large language models (LLMs) have shown promise in diagnostic dialogue, their evaluation has been largely restricted to text-only interactions, failing to capture the complexity of modern remote care delivery.

Abstract The large volume of abdominal computed tomography (CT) scans1,2 coupled with the shortage of radiologists3,4,5,6 have intensified the need for automated medical image analysis tools. Previous state-of-the-art approaches for automated analysis leverage vision–language models (VLMs) that jointly model images and radiology reports7,8,9,10,11,12. However, current medical VLMs are generally limited to 2D images and short reports.