Every year the frontier models get better, and every year specialized medical models stay ahead on medical question answering, clinical extraction and other tasks a production deployment depends on. This keynote shares a current scorecard, comparing medical language models against the latest frontier LLMs on three axes:

• Clinical accuracy: medical question answering and clinical extraction
• Inference cost: fixed-cost single-GPU deployment against API pricing
• Responsible AI: bias, fairness, safety and reliability, measured against benchmarks derived from regulation

We then turn from models to architecture. Regulatory-grade healthcare AI is built on a governed clinical data foundation: a unified patient record that many accurate, auditable agents reach through an open protocol like MCP, so each new application becomes a configuration change instead of a data engineering project.