335 citations · 1.3k across the 25 of their papers we have counts for
25 papers
ResidencyRL: Reinforcement Learning in Simulated Clinical Environments
Valentin Liévin, Samuel Schmidgall, Tim Strother +32
In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of fee…
MedGemma Technical Report
Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri +78
Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks,…
TxGemma: Efficient and Agentic LLMs for Therapeutics
Eric Wang, Samuel Schmidgall, Paul F. Jaeger +6
Therapeutic development is a costly and high-risk endeavor that is often plagued by high failure rates. To address this, we introduce TxGemma, a suite of efficient, generalist larg…
Health AI Developer Foundations
Atilla P. Kiraly, Sebastien Baur, Kenneth Philbrick +23
Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel…
Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models
Yanchen Wang, Adam Turnbull, Tiange Xiang +6
Neural decoding, the process of understanding how brain activity corresponds to different stimuli, has been a primary objective in cognitive sciences. Over the past three decades,…
Tx-LLM: A Large Language Model for Therapeutics
Juan Manuel Zambrano Chaves, Eric Wang, Tao Tu +7
Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be inval…