Microsoft Health Futures
News & features
PadChest-GR: A bilingual grounded radiology reporting benchmark for chest X-rays
| Daniel Coelho de Castro and Javier Alvarez-Valle
The world’s first multimodal, bilingual radiology dataset could reshape the way radiologists and AI systems make sense of X-rays. PadChest-GR, developed by the University of Alicante with Microsoft Research, has the potential to advance research across the field for years…
Abstracts: Zero-shot models in single-cell biology with Alex Lu
| Gretchen Huizinga and Alex Lu
The emergence of foundation models has sparked interest in applications to single-cell biology, but when tested in zero-shot settings, they underperform compared to simpler methods. Alex Lu shares insights on why more research on AI models is needed in biological…
Collaborators: Healthcare Innovation to Impact
| Matthew Lungren, Jonathan M. Carlson, Smitha Saligrama, Will Guyman, and Cameron Runde
In this discussion, Matthew Lungren, Jonathan Carlson, Smitha Saligrama, Will Guyman, and Cameron Runde explore how teams across Microsoft are working together to generate advanced AI capabilities and solutions for developers and clinicians around the globe.
Philip Rosenfield, Alex X. Lu, Ava P. Amini, Lorin Crawford, Kasia Z. Kedzierska Single-cell foundation models are an exciting paradigm for biologists, as they may accelerate the understanding of complex cell data and reveal previously unknown biology. Single-cell foundation models…
Research Focus: Week of April 7, 2025
In this issue: We introduce a new dataset designed to assist renewable energy infrastructure planners, a new method for denoising MRI imagery, and an AI tool for analyzing distant galaxies. Check out our latest research and other updates.
Research Focus: Week of March 24, 2025
In this issue, we examine a new conversation segmentation method that delivers more coherent and personalized agent conversation, and we review efforts to improve MLLMs’ understanding of geologic maps. Check out the latest research and other updates.
Advancing biomedical discovery: Overcoming data challenges in precision medicine
| Mandi Hall
Our recent study in Nature Scientific Reports identified key challenges in the biomedical data lifecycle and offered 7 actionable recommendations.
This panel explores the transformative potential of generative AI in learning the language of nature and patients for precision health, from proteins to medical imaging, from electronic medical records to home health monitoring.
Hoifung Poon introduces an agenda in precision health, utilizing generative AI to pretrain high-fidelity patient embeddings from multimodal, longitudinal patient journeys. This approach unlocks population-scale real-world evidence, optimizing clinical care and accelerating biomedical discovery.