The illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks
Gu, Yu, Fu, Jingjing, Liu, Xiaodong, et al.
preprint2025
arXiv arXiv:2509.18234
View paper →
Science · Solutions · Society · Soul
Yu Gu
where models meet meaning .Making intelligence useful — in health, science, and beyond
About
I'm Yu Gu (also Aiden Gu; Chinese: 顾禹). I design LLMs, multimodal foundation models, and agentic AI systems. Currently Principal Scientist at Microsoft Research and Health & Life Sciences, working at the intersection of AI, healthcare, and scientific discovery.
My work appears in major science/medicine journals (Nature/Cell/Nature Medicine, etc.) and major AI conferences (ICML/NeurIPS/ICLR/CVPR, etc.). I led the production rollout of Microsoft’s Health AI platform, including Healthcare Agent Orchestrator and enterprise AI solutions such as MedImageInsight/MedImageParse/CXRReportGen. Previously founding team at a Series C–acquired AI startup. I serve as area chair for major AI conferences (ICML/NeurIPS, etc.) and reviewer for the Nature portfolio.
Featured
Selected highlights from recent research.
Magma: A Foundation Model for Multimodal AI Agents
Jianwei Yang, Reuben Tan, Qianhui Wu, et al.
preprint2025
CVPR
View paper →
Biomedjourney: Counterfactual biomedical image generation by instruction-learning from multimodal patient journeys
Gu, Yu, Yang, Jianwei, Usuyama, Naoto, et al.
preprint2024
arXiv arXiv:2310.10765
View paper →
A whole-slide foundation model for digital pathology from real-world data
Xu, Hanwen, Usuyama, Naoto, Bagga, Jaspreet, et al.
journal2024
Nature
View paper →
A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities
Zhao, Theodore*, Gu, Yu*, Yang, Jianwei, et al.
journal2024
Nature methods
View paper →
Domain-specific language model pretraining for biomedical natural language processing
Gu, Yu, Tinn, Robert, Cheng, Hao, et al.
journal2022
ACM
View paper →
Publications
Research papers, conference proceedings, and scholarly contributions.
Scaling medical imaging report generation with multimodal reinforcement learning
Qianchu Liu, Sheng Zhang, ... Hoifung Poon
Unknown Venue (2026)
preprint2026
CV
X-reasoner: Towards generalizable reasoning across modalities and domains
Liu, Qianchu, Zhang, Sheng, ... et al.
arXiv arXiv:2505.03981 (2025)
preprint2025
Research
Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale
Wong, Cliff, Preston, Sam, ... et al.
arXiv arXiv:2502.00943 (2025)
preprint2025
Research
The illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks
Gu, Yu, Fu, Jingjing, ... et al.
arXiv arXiv:2509.18234 (2025)
preprint2025
Research
QuantRad: Advancing Quantitative Reliability in Radiology Report Generation with Cascaded Decoders
Jin, Ying, Codella, Noel C, ... Hwang, Jenq-Neng
arXiv arXiv:x (2025)
preprint2025
Research
Media Coverage
Press features, interviews, and notable mentions from industry leaders.
BusinessWire·October 21, 2025
Amalgam Rx's Medical-Grade AI Doubles Digital Health Engagement — Validated in Peer-Reviewed Studies
Forbes·October 3, 2025
AI Doctors Cheat Medical Tests
GeekWire·June 15, 2025
Microsoft and Providence Create AI That Unlocks Tumor Insights at a Scale Previously Out of Reach
CNBCTV18·June 15, 2025
Microsoft AI Breakthrough in Cancer Research: All About GigaTime
Forbes·May 22, 2025
Stanford's Use of Microsoft Agentic Platform Leads to Better Analysis
Forbes·October 10, 2024
Microsoft Announces Numerous New AI Tools Dedicated To Healthcare
MarkTechPost·May 25, 2024
Microsoft Research Introduces GigaPath: A Novel Vision Transformer for Digital Pathology
Forbes·May 22, 2024
Microsoft Announces New Foundation Model for Digital Pathology, Diving Deeper into Clinical Medicine
VentureBeat·September 15, 2021
Microsoft Researchers Claim State-of-the-Art Biomedical NLP Model
Areas of Focus
Active research directions and ongoing work.
Large Language Models
active2020–Present
Building and adapting domain-specific LLMs for biomedical NLP and real-world healthcare tasks, including pretraining, fine-tuning, and evaluation.
Publications:
Domain-specific language model pretraining for biomedical natural language processingFine-tuning large neural language models for biomedical natural language processingUniversalner: Targeted distillation from large language models for open named entity recognitionScaling clinical trial matching using large language models: a case study in oncology
LLMPretrainingBiomedical NLPEvaluation
Multimodal Foundation Models
active2022–Present
Designing and stress-testing vision-language foundation models across medical imaging and multimodal benchmarks at scale.
Publications:
Magma: A Foundation Model for Multimodal AI AgentsA foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalitiesA whole-slide foundation model for digital pathology from real-world dataBiomedjourney: Counterfactual biomedical image generation by instruction-learning from multimodal patient journeysBiomedParse: a biomedical foundation model for image parsing of everything everywhere all at onceThe illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks
MultimodalFoundation ModelsVision-LanguageMedical Imaging
Reasoning
active2024–Present
Advancing generalizable reasoning across modalities and domains, and targeted distillation for robust information extraction.
Publications:
ReasoningGeneralizationDistillation
Agents
active2024–Present
Developing multimodal AI agents and workflows that orchestrate tools and reasoning to act in complex real-world settings.
Publications:
AgentsTool UsePlanningMultimodal
News & Updates
Recent publications, presentations, and milestones across research and collaborations.
📄
The Illusion of Readiness: Stress Testing Large Frontier Models on Multimodal Medical Benchmarks
September 18, 2025The Illusion of Readiness: Stress Testing Large Frontier Models on Multimodal Medical Benchmarks. In just the first week, the paper has sparked meaningful conversation — highlighted by Eric Topol, shared across Health AI communities, and prompting outreach from Science and Health AI leaders looking for what comes next.
Related: The illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks →🎤
Announcing QRad
September 2, 2025We're excited to announce QRad, accepted to NeurIPS - The Second Workshop on GenAI for Health: Potential, Trust, and Policy Compliance, a new project that enhances radiology report generation by captioning-to-VQA reframing.
Related: QRad: Enhancing Radiology Report Generation by Captioning-to-VQA Reframing →🎤
BiomedParse topped the CVPR 2025 3D Biomedical Image Segmentation Challenge! Our model delivered best-in-class performance across 42 tasks spanning CT, MRI, PET, ultrasound, and microscopy. check out the announcement
Related: A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities →7 more updates
Connect
Models, teams, and a dream — often in that order.