arXiv AI
BioMed-Agent-RL: A Meta Learning, All You Need for Biomedical Applications
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
Oct 1
From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning
arXiv:2609.39566v1 Announce Type: new Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected e...
arXiv AI
Jun 30
An AI agent for treatment reasoning over a biomedical tool universe
arXiv:2606. 28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy.
arXiv AI
Aug 10
ResidencyRL: Reinforcement Learning in Simulated Clinical Environments
arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.
arXiv AI
Aug 13
Teaching agentic AI to learn expert reasoning for rare disease diagnosis
arXiv:2606. 16149v3 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) rank the correct disease first in only 35.
arXiv Machine Learning
Sep 22
Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards
arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conv...
arXiv AI
Jul 17
CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation
arXiv:2510. 21324v2 Announce Type: replace Abstract: Chest X-ray (CXR) plays a pivotal role in clinical diagnosis, and a variety of task-specific and foundation models have been developed for automatic CXR interpretation.