Welcome to Wizardlancet’s

About Me

Dr. Zilong Wang is currently a Senior Researcher at the Machine Learning Group, Microsoft Research Asia (MSRA) in Shanghai, China. His research lies at the intersection of AI4Health, Foundation Models, and Human-AI Interaction, with a focus on building reliable, generalizable, and clinically grounded AI systems for real-world healthcare. Trained as a physician, he received his M.D. in Clinical Medicine from Shanghai Medical College, Fudan University in 2018. This clinical background shapes his research philosophy: advancing computational models that are not only technically sophisticated, but also robust to real-world heterogeneity, aligned with clinical reasoning, and safely integrated into healthcare workflows.

His work spans three tightly connected directions. In AI4Health, he develops medical computer vision and multimodal systems for screening, diagnosis, and longitudinal disease management. In Foundation Models, he studies multimodal and large language model (LLM) architectures, evaluation frameworks, and reinforcement learning methods that improve generalization, interpretability, and clinical faithfulness. In Human-AI Interaction, he studies how advanced AI systems—including multimodal and agentic models—interact with people in real-world settings, spanning clinical workflows as well as accessibility and aging scenarios. He focuses on human-in-the-loop designs that let users query, verify, correct, and steer AI behavior, making systems more transparent, controllable, and trustworthy under uncertainty and high-stakes constraints.

Prior to joining MSRA in 2023, Dr. Wang served as CTO of medical technology startups, where he led the development of multiple AI Software as a Medical Device (SaMD) products and successfully drove them through clinical validation, regulatory approval, and market access. He was recognized by the Forbes China 30 Under 30 (2020) and the Hurun U30 China Entrepreneurship Leaders (2021), and serves on the Executive Committee of the CCF Digital Medicine Technical Committee.

News

[Feb. 2026] We released OMGs (Ovarian tumour Multidisciplinary intelligent aGent System), an LLM-powered multi-agent framework designed to support MDT decision-making across the ovarian tumor care continuum. In multicenter evaluations, OMGs achieved performance comparable to expert MDT consensus, demonstrating the potential of collaborative agentic AI systems in high-stakes clinical decision support.

[Jan. 2026] We introduced GI-Bench, a panoramic benchmark covering 20 fine-grained lesion categories to systematically evaluate Multimodal Large Language Models (MLLMs) across a five-stage gastrointestinal endoscopy clinical workflow, advancing clinically grounded evaluation of multimodal foundation models.

[Aug. 2025] We open-sourced Agent Lightning⚡, a flexible framework that enables developers to train ANY AI agent with Reinforcement Learning (RL). By decoupling agent execution from model training, it supports seamless integration with existing frameworks such as LangChain, AutoGen, and CrewAI with minimal code modification.

[Aug. 2025] We released preprints for two new medical foundation models: RenalCLIP, a disease-centric vision-language foundation model for precision oncology in kidney cancer, and DermINO, a dermatology foundation model based on a novel multi-view hybrid pretraining strategy for robust visual representation learning.

Contact

• Email: wangzilong@microsoft.com
• GitHub: wizardlancet

Selected Publications