🏥 Deploying agents in healthcare and life sciences means answering for audit trails and patient safety, not just product quality. Here's what three real deployments have in common. Title: Scaling Agents in Healthcare & Life Sciences: Lessons from Madrigal Pharmaceuticals, Abridge, and Vizient URL: LangChain's analysis of the industry finds that the teams furthest along build observability and evaluation infrastructure alongside the agents themselves. Three case studies make the pattern concrete. Highlight ①💊 Madrigal Pharmaceuticals Normalized scattered data formats into a warehouse and rebuilt around Deep Agents with an orchestrator plus modular skills. New use-case development dropped from weeks to hours, and deployment shrank from months to weeks. Highlight ②🩺 Abridge As its clinical-documentation agent scaled to 250+ health systems, the team built LLM judges around quality pillars and a tiered release process. Judge creation dropped from days to hours, release cycles from 1-2 months to days, and accuracy/completeness improved by 17% and 19%. Highlight ③🏢 Vizient Replaced siloed multi-agent coordination with a supervisor-led hierarchy, separating prompts from code. That unlocked real-time diagnosis of errors and much faster onboarding of new data sources. The lesson: baking trust in from day one, instead of bolting it on later, is actually the fast path to greater agent autonomy. #AIAgents# #HealthcareAI#

🏥 A rice-grain robot dissolves kidney stones. Canadian researchers steer it with magnets, no cuts. Big surgery, tiny robot. Sources: U of T, Sci Robotics #omgfacts# #KidneyStones# #Robotics#