Math meets medicine

General purpose LLMs excel at solving math exams but struggle with complicated real-world healthcare reasoning. Learning to reason from complex healthcare histories, recent medical research, and laboratory results requires a specialized reasoning agent with embedded knowledge of millions of related healthcare cases.

Policies define the protocol followed by a machine learning model and are learned from human guidance. General purpose LLM policies are defined by non-healthcare-trained individuals, but optimizing policies through feedback from healthcare professionals enables learning protocol from the best in class.

AI systems are inherently bad at knowing when they don't know. Building interpretability into AI allows healthcare professionals to understand the reasoning process and locate the source of its output, while alignment AI models interpret the output of other AI models at scale, flagging inconsistencies before they harm.