Avinatan Hassidim

Supplement 1. eMethods. Exploratory analysis eTable. Secondary and exploratory comparisons of model performance against clinical standard procedure (Hadlock formula) eFigure 2. Component interaction diagram of the study exam system eFigure 3. Distribution of participants per number of blind sweep examinations in Chicago Supplement 2. Figure 1. Flow Diagram of Patient-Level Data Flow for Primary Analysis (opens in new tab) GA indicates gestational age. Figure 2.

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 Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge.