Joelle Barral

Abstract While large language models (LLMs) have shown promise in diagnostic dialogue1, their capabilities for effective management reasoning—including disease progression, therapeutic response, and safe medication prescription—remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE)1−3 through a new LLM-based agentic system optimized for multi-visit clinical management and dialogue.

Abstract Real-world clinical practice is inherently multimodal, relying on the synthesis of patient history with visual information such as medical imagery and clinical documents. Although large language models (LLMs) have shown promise in diagnostic dialogue, their evaluation has been largely restricted to text-only interactions, failing to capture the complexity of modern remote care delivery.

1 day ago More than 8 million people in the U.S. take blood thinners to prevent dangerous clots. At the same time, the drugs are responsible for a staggering number of serious bleeding events. Larry Bordeaux, 65, has been on blood thinners since 2010, after he started developing deadly clots following an …